Deep Learning Models for Medical Imaging
Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evoluti...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Santosh, K. C. [verfasserIn] |
|---|
| Format: |
E-Book |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
San Diego: Elsevier Science & Technology ; 2021 |
|---|
| Schlagwörter: |
Artificial intelligence, Medical applications Deep learning (Machine learning) |
|---|---|
| Formangabe: |
Electronic books |
| Anmerkung: |
Description based upon print version of record 4.7.3.2 Results with data augmentation |
|---|---|
| Umfang: |
1 Online-Ressource (172 p) |
| Reihe: |
Primers in Biomedical Imaging Devices and Systems Ser |
|---|
| Links: | |
|---|---|
| ISBN: |
0-12-823650-7 978-0-12-823650-5 |
| Katalog-ID: |
1782505687 |
|---|
| LEADER | 01000cam a22002652 4500 | ||
|---|---|---|---|
| 001 | 1782505687 | ||
| 003 | DE-627 | ||
| 005 | 20240503140114.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 211217s2021 xxu|||||o 00| ||eng c | ||
| 020 | |a 0128236507 |c electronic publication |9 0-12-823650-7 | ||
| 020 | |a 9780128236505 |c electronic bk. |9 978-0-12-823650-5 | ||
| 035 | |a (DE-627)1782505687 | ||
| 035 | |a (DE-599)KEP069017158 | ||
| 035 | |a (ELSEVIER)on1267766379 | ||
| 035 | |a (EBP)069017158 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 044 | |c XD-US | ||
| 050 | 0 | |a RC78.7.D53 | |
| 082 | 0 | |a 616.0754 |2 23 | |
| 100 | 1 | |a Santosh, K. C. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Deep Learning Models for Medical Imaging |
| 264 | 1 | |a San Diego |b Elsevier Science & Technology |c 2021 | |
| 300 | |a 1 Online-Ressource (172 p) | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 490 | 0 | |a Primers in Biomedical Imaging Devices and Systems Ser | |
| 500 | |a Description based upon print version of record | ||
| 500 | |a 4.7.3.2 Results with data augmentation | ||
| 520 | |a Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evolution of machine learning -- 1.3.1 Rule-based learning -- 1.3.2 Feature-based learning -- 1.3.3 Representation learning -- 1.4 Basics to deep learning -- 1.4.1 The rise of cybernetics -- 1.4.2 The connectionist movement -- 1.4.3 The onset of deep learning -- 1.4.4 Motivation: deep learning | ||
| 520 | |a 1.5 Importance of deep learning -- 1.6 Deep learning in medical imaging: a review -- 1.6.1 Medical imaging scope -- 1.6.2 Medical imaging data -- 1.6.3 Applications: deep learning in medical imaging -- 1.7 Scope of the book -- References -- 2 Deep learning: a review -- 2.1 Background -- 2.2 Artificial neural networks -- 2.2.1 The neuron -- 2.2.2 Activation functions -- 2.2.3 Multilayer feed forward neural network -- 2.2.4 Training neural networks by back-propagation -- 2.2.5 Optimization -- 2.2.5.1 Objective functions -- Mean squared error -- Cross-entropy measures | ||
| 520 | |a 2.2.5.2 Optimization techniques -- Stochastic gradient descent -- Momentum -- Adaptive learning rates -- 2.2.6 Regularization -- 2.3 Convolutional neural networks -- 2.3.1 Feature extraction using convolutions -- 2.3.2 Subsampling -- 2.3.3 Effect of nonlinearity on activation maps -- 2.3.4 Layer design -- 2.3.5 Output layer -- 2.4 Encoder-decoder architecture -- 2.4.1 Unsupervised learning in CNNs -- 2.4.2 Image-to-image translation -- 2.4.3 Localization -- 2.4.4 Multiscale feature propagation -- References -- 3 Deep learning models -- 3.1 Deep learning models | ||
| 520 | |a 3.1.1 Learning different objectives -- 3.1.2 Network structure for CNNs -- 3.1.3 Types of models based on learning strategies -- 3.2 Elements in deep learning pipeline -- 3.2.1 Data preprocessing -- 3.2.2 Model selection -- 3.2.3 Model validation and hyperparameter tuning -- 3.3 Evolution of deep learning models and applications -- 3.3.1 Classification -- 3.3.2 Localization -- 3.3.3 Segmentation -- References -- 4 Cytology image analysis -- 4.1 Background -- 4.2 Cytology: a brief overview -- 4.3 Types of cytology -- 4.4 Cytology slide preparation -- 4.4.1 Aspiration cytology | ||
| 520 | |a 4.4.2 Exfoliative cytology -- 4.4.3 Abrasive cytology -- 4.4.4 Specimen collection -- 4.4.5 Slide preparation -- 4.4.6 Fixation techniques and staining protocol -- 4.5 Cytological process and digitization -- 4.6 Cervical cell cytology -- 4.6.1 Modalities of cervical specimen collection -- 4.6.2 Characteristics of cytomorphology of malignant cells -- 4.7 Experiments -- 4.7.1 Dataset -- 4.7.2 Experimental setup and protocols -- 4.7.2.1 Transfer learning: a quick overview -- 4.7.3 Results and discussion -- 4.7.3.1 Results with or without using transfer learning | ||
| 650 | 0 | |a Diagnostic imaging | |
| 650 | 0 | |a Artificial intelligence |x Medical applications | |
| 650 | 0 | |a Deep learning (Machine learning) | |
| 650 | 2 | |a Diagnostic Imaging |0 (DNLM)D003952 | |
| 650 | 2 | |a Deep Learning |0 (DNLM)D000077321 | |
| 650 | 4 | |a Imagerie pour le diagnostic |0 (CaQQLa)201-0146124 | |
| 650 | 4 | |a Intelligence artificielle en medecine |0 (CaQQLa)201-0180593 | |
| 650 | 4 | |a Apprentissage profond |0 (CaQQLa)000311746 | |
| 650 | 4 | |a Artificial intelligence - Medical applications |0 (OCoLC)fst00817267 | |
| 650 | 4 | |a Diagnostic imaging |0 (OCoLC)fst00892354 | |
| 655 | 0 | |a Electronic books | |
| 700 | 1 | |a Das, Nibaran |4 oth | |
| 700 | 1 | |a Ghosh, Swarnendu |4 oth | |
| 776 | 1 | |z 9780128236505 | |
| 776 | 1 | |z 0128236507 | |
| 776 | 1 | |z 9780128235041 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Santosh, K. C |t Deep Learning Models for Medical Imaging |d San Diego : Elsevier Science & Technology,c2021 |z 9780128235041 |
| 856 | 4 | 0 | |u https://www.sciencedirect.com/science/book/9780128235041 |m X:ELSEVIER |x Verlag |z lizenzpflichtig |
| 856 | 4 | 2 | |u https://www.gbv.de/dms/bowker/toc/9780128235041.pdf |v 2022-07-28 |x Aggregator |3 Inhaltsverzeichnis |
| 912 | |a BSZ-33-ESD-L1FH | ||
| 912 | |a GBV-33-Freedom |b 2021 | ||
| 912 | |a GBV-33-Freedom |b 2022 | ||
| 912 | |a ZDB-33-EBS | ||
| 912 | |a ZDB-33-EGE |b 2021 | ||
| 912 | |a GBV-33-Freedom |b 2023 | ||
| 912 | |a ZDB-33-ESD | ||
| 912 | |a GBV-33-EBS-HST | ||
| 912 | |a BSZ-33-EBS-C1UB | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a ISIL_DE-705 | ||
| 912 | |a SYSFLAG_1 | ||
| 912 | |a GBV_KXP | ||
| 912 | |a GBV_ILN_132 | ||
| 912 | |a ISIL_DE-959 | ||
| 912 | |a GBV_ILN_185 | ||
| 912 | |a ISIL_DE-Sra5 | ||
| 912 | |a GBV_ILN_370 | ||
| 912 | |a ISIL_DE-1373 | ||
| 912 | |a GBV_ILN_2020 | ||
| 912 | |a ISIL_DE-Ch1 | ||
| 912 | |a GBV_ILN_2057 | ||
| 912 | |a ISIL_DE-L189 | ||
| 951 | |a BO | ||
| 953 | |2 045F |a 616.0754 | ||
| 980 | |2 60 |1 01 |x 0705 |b 4236042169 |c 00 |f --%%-- |d --%%-- |e s |j --%%-- |k Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. |k Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung |y z |z 20-12-22 | ||
| 980 | |2 132 |1 01 |x 0959 |b 4500007792 |h EBS Elsevier |k Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. |k Zeitlich begrenzte Lizenzierung |y k |z 13-03-24 | ||
| 980 | |2 185 |1 01 |x 3519 |b 4514762105 |h OLR-EBS |k Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. |y z |z 23-04-24 | ||
| 980 | |2 370 |1 01 |x 4370 |b 4540285246 |h EBS Elsevier |k Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. |u i |y z |z 20-06-24 | ||
| 980 | |2 2020 |1 01 |x DE-Ch1 |b 452034056X |c 00 |f --%%-- |d --%%-- |e n |j n |k Campuslizenz |y l01 |z 03-05-24 | ||
| 980 | |2 2057 |1 01 |x DE-L189 |b 4467496580 |c 00 |f --%%-- |d --%%-- |e --%%-- |j n |k Campuslizenz |y l01 |z 24-01-24 | ||
| 981 | |2 60 |1 01 |x 0705 |r https://www.sciencedirect.com/science/book/9780128235041 | ||
| 981 | |2 132 |1 01 |x 0959 |y Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz |r https://www.sciencedirect.com/science/book/9780128235041 | ||
| 981 | |2 185 |1 01 |x 3519 |r https://www.sciencedirect.com/science/book/9780128235041 | ||
| 981 | |2 370 |1 01 |x 4370 |y E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich |r https://www.sciencedirect.com/science/book/9780128235041 | ||
| 981 | |2 2020 |1 01 |x DE-Ch1 |r https://www.sciencedirect.com/science/book/9780128235041 | ||
| 981 | |2 2057 |1 01 |x DE-L189 |y HTWK-Zugang |r https://www.sciencedirect.com/science/book/9780128235041 | ||
| 982 | |2 132 |1 01 |x 0959 |8 00 |a EBooks Elsevier Engineering | ||
| 995 | |2 132 |1 01 |x 0959 |a EBS Elsevier | ||
| 995 | |2 185 |1 01 |x 3519 |a OLR-EBS | ||
| 995 | |2 370 |1 01 |x 4370 |a EBS Elsevier | ||
| author_variant |
k c s kc kcs |
|---|---|
| matchkey_str |
book:9780128236505:2021---- |
| oclc_num |
(ELSEVIER)1267766379 |
| hierarchy_sort_str |
2021 |
| callnumber-subject-code |
RC |
| publishDate |
2021 |
| allfields |
0128236507 electronic publication 0-12-823650-7 9780128236505 electronic bk. 978-0-12-823650-5 (DE-627)1782505687 (DE-599)KEP069017158 (ELSEVIER)on1267766379 (EBP)069017158 DE-627 ger DE-627 rakwb eng XD-US RC78.7.D53 616.0754 23 Santosh, K. C. verfasserin aut Deep Learning Models for Medical Imaging San Diego Elsevier Science & Technology 2021 1 Online-Ressource (172 p) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Primers in Biomedical Imaging Devices and Systems Ser Description based upon print version of record 4.7.3.2 Results with data augmentation Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evolution of machine learning -- 1.3.1 Rule-based learning -- 1.3.2 Feature-based learning -- 1.3.3 Representation learning -- 1.4 Basics to deep learning -- 1.4.1 The rise of cybernetics -- 1.4.2 The connectionist movement -- 1.4.3 The onset of deep learning -- 1.4.4 Motivation: deep learning 1.5 Importance of deep learning -- 1.6 Deep learning in medical imaging: a review -- 1.6.1 Medical imaging scope -- 1.6.2 Medical imaging data -- 1.6.3 Applications: deep learning in medical imaging -- 1.7 Scope of the book -- References -- 2 Deep learning: a review -- 2.1 Background -- 2.2 Artificial neural networks -- 2.2.1 The neuron -- 2.2.2 Activation functions -- 2.2.3 Multilayer feed forward neural network -- 2.2.4 Training neural networks by back-propagation -- 2.2.5 Optimization -- 2.2.5.1 Objective functions -- Mean squared error -- Cross-entropy measures 2.2.5.2 Optimization techniques -- Stochastic gradient descent -- Momentum -- Adaptive learning rates -- 2.2.6 Regularization -- 2.3 Convolutional neural networks -- 2.3.1 Feature extraction using convolutions -- 2.3.2 Subsampling -- 2.3.3 Effect of nonlinearity on activation maps -- 2.3.4 Layer design -- 2.3.5 Output layer -- 2.4 Encoder-decoder architecture -- 2.4.1 Unsupervised learning in CNNs -- 2.4.2 Image-to-image translation -- 2.4.3 Localization -- 2.4.4 Multiscale feature propagation -- References -- 3 Deep learning models -- 3.1 Deep learning models 3.1.1 Learning different objectives -- 3.1.2 Network structure for CNNs -- 3.1.3 Types of models based on learning strategies -- 3.2 Elements in deep learning pipeline -- 3.2.1 Data preprocessing -- 3.2.2 Model selection -- 3.2.3 Model validation and hyperparameter tuning -- 3.3 Evolution of deep learning models and applications -- 3.3.1 Classification -- 3.3.2 Localization -- 3.3.3 Segmentation -- References -- 4 Cytology image analysis -- 4.1 Background -- 4.2 Cytology: a brief overview -- 4.3 Types of cytology -- 4.4 Cytology slide preparation -- 4.4.1 Aspiration cytology 4.4.2 Exfoliative cytology -- 4.4.3 Abrasive cytology -- 4.4.4 Specimen collection -- 4.4.5 Slide preparation -- 4.4.6 Fixation techniques and staining protocol -- 4.5 Cytological process and digitization -- 4.6 Cervical cell cytology -- 4.6.1 Modalities of cervical specimen collection -- 4.6.2 Characteristics of cytomorphology of malignant cells -- 4.7 Experiments -- 4.7.1 Dataset -- 4.7.2 Experimental setup and protocols -- 4.7.2.1 Transfer learning: a quick overview -- 4.7.3 Results and discussion -- 4.7.3.1 Results with or without using transfer learning Diagnostic imaging Artificial intelligence Medical applications Deep learning (Machine learning) Diagnostic Imaging (DNLM)D003952 Deep Learning (DNLM)D000077321 Imagerie pour le diagnostic (CaQQLa)201-0146124 Intelligence artificielle en medecine (CaQQLa)201-0180593 Apprentissage profond (CaQQLa)000311746 Artificial intelligence - Medical applications (OCoLC)fst00817267 Diagnostic imaging (OCoLC)fst00892354 Electronic books Das, Nibaran oth Ghosh, Swarnendu oth 9780128236505 0128236507 9780128235041 Erscheint auch als Druck-Ausgabe Santosh, K. C Deep Learning Models for Medical Imaging San Diego : Elsevier Science & Technology,c2021 9780128235041 https://www.sciencedirect.com/science/book/9780128235041 X:ELSEVIER Verlag lizenzpflichtig https://www.gbv.de/dms/bowker/toc/9780128235041.pdf 2022-07-28 Aggregator Inhaltsverzeichnis BSZ-33-ESD-L1FH GBV-33-Freedom 2021 GBV-33-Freedom 2022 ZDB-33-EBS ZDB-33-EGE 2021 GBV-33-Freedom 2023 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_60 ISIL_DE-705 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2057 ISIL_DE-L189 BO 045F 616.0754 60 01 0705 4236042169 00 --%%-- --%%-- s --%%-- Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung z 20-12-22 132 01 0959 4500007792 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514762105 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540285246 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 452034056X 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2057 01 DE-L189 4467496580 00 --%%-- --%%-- --%%-- n Campuslizenz l01 24-01-24 60 01 0705 https://www.sciencedirect.com/science/book/9780128235041 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128235041 185 01 3519 https://www.sciencedirect.com/science/book/9780128235041 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128235041 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128235041 2057 01 DE-L189 HTWK-Zugang https://www.sciencedirect.com/science/book/9780128235041 132 01 0959 00 EBooks Elsevier Engineering 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| spelling |
0128236507 electronic publication 0-12-823650-7 9780128236505 electronic bk. 978-0-12-823650-5 (DE-627)1782505687 (DE-599)KEP069017158 (ELSEVIER)on1267766379 (EBP)069017158 DE-627 ger DE-627 rakwb eng XD-US RC78.7.D53 616.0754 23 Santosh, K. C. verfasserin aut Deep Learning Models for Medical Imaging San Diego Elsevier Science & Technology 2021 1 Online-Ressource (172 p) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Primers in Biomedical Imaging Devices and Systems Ser Description based upon print version of record 4.7.3.2 Results with data augmentation Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evolution of machine learning -- 1.3.1 Rule-based learning -- 1.3.2 Feature-based learning -- 1.3.3 Representation learning -- 1.4 Basics to deep learning -- 1.4.1 The rise of cybernetics -- 1.4.2 The connectionist movement -- 1.4.3 The onset of deep learning -- 1.4.4 Motivation: deep learning 1.5 Importance of deep learning -- 1.6 Deep learning in medical imaging: a review -- 1.6.1 Medical imaging scope -- 1.6.2 Medical imaging data -- 1.6.3 Applications: deep learning in medical imaging -- 1.7 Scope of the book -- References -- 2 Deep learning: a review -- 2.1 Background -- 2.2 Artificial neural networks -- 2.2.1 The neuron -- 2.2.2 Activation functions -- 2.2.3 Multilayer feed forward neural network -- 2.2.4 Training neural networks by back-propagation -- 2.2.5 Optimization -- 2.2.5.1 Objective functions -- Mean squared error -- Cross-entropy measures 2.2.5.2 Optimization techniques -- Stochastic gradient descent -- Momentum -- Adaptive learning rates -- 2.2.6 Regularization -- 2.3 Convolutional neural networks -- 2.3.1 Feature extraction using convolutions -- 2.3.2 Subsampling -- 2.3.3 Effect of nonlinearity on activation maps -- 2.3.4 Layer design -- 2.3.5 Output layer -- 2.4 Encoder-decoder architecture -- 2.4.1 Unsupervised learning in CNNs -- 2.4.2 Image-to-image translation -- 2.4.3 Localization -- 2.4.4 Multiscale feature propagation -- References -- 3 Deep learning models -- 3.1 Deep learning models 3.1.1 Learning different objectives -- 3.1.2 Network structure for CNNs -- 3.1.3 Types of models based on learning strategies -- 3.2 Elements in deep learning pipeline -- 3.2.1 Data preprocessing -- 3.2.2 Model selection -- 3.2.3 Model validation and hyperparameter tuning -- 3.3 Evolution of deep learning models and applications -- 3.3.1 Classification -- 3.3.2 Localization -- 3.3.3 Segmentation -- References -- 4 Cytology image analysis -- 4.1 Background -- 4.2 Cytology: a brief overview -- 4.3 Types of cytology -- 4.4 Cytology slide preparation -- 4.4.1 Aspiration cytology 4.4.2 Exfoliative cytology -- 4.4.3 Abrasive cytology -- 4.4.4 Specimen collection -- 4.4.5 Slide preparation -- 4.4.6 Fixation techniques and staining protocol -- 4.5 Cytological process and digitization -- 4.6 Cervical cell cytology -- 4.6.1 Modalities of cervical specimen collection -- 4.6.2 Characteristics of cytomorphology of malignant cells -- 4.7 Experiments -- 4.7.1 Dataset -- 4.7.2 Experimental setup and protocols -- 4.7.2.1 Transfer learning: a quick overview -- 4.7.3 Results and discussion -- 4.7.3.1 Results with or without using transfer learning Diagnostic imaging Artificial intelligence Medical applications Deep learning (Machine learning) Diagnostic Imaging (DNLM)D003952 Deep Learning (DNLM)D000077321 Imagerie pour le diagnostic (CaQQLa)201-0146124 Intelligence artificielle en medecine (CaQQLa)201-0180593 Apprentissage profond (CaQQLa)000311746 Artificial intelligence - Medical applications (OCoLC)fst00817267 Diagnostic imaging (OCoLC)fst00892354 Electronic books Das, Nibaran oth Ghosh, Swarnendu oth 9780128236505 0128236507 9780128235041 Erscheint auch als Druck-Ausgabe Santosh, K. C Deep Learning Models for Medical Imaging San Diego : Elsevier Science & Technology,c2021 9780128235041 https://www.sciencedirect.com/science/book/9780128235041 X:ELSEVIER Verlag lizenzpflichtig https://www.gbv.de/dms/bowker/toc/9780128235041.pdf 2022-07-28 Aggregator Inhaltsverzeichnis BSZ-33-ESD-L1FH GBV-33-Freedom 2021 GBV-33-Freedom 2022 ZDB-33-EBS ZDB-33-EGE 2021 GBV-33-Freedom 2023 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_60 ISIL_DE-705 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2057 ISIL_DE-L189 BO 045F 616.0754 60 01 0705 4236042169 00 --%%-- --%%-- s --%%-- Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung z 20-12-22 132 01 0959 4500007792 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514762105 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540285246 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 452034056X 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2057 01 DE-L189 4467496580 00 --%%-- --%%-- --%%-- n Campuslizenz l01 24-01-24 60 01 0705 https://www.sciencedirect.com/science/book/9780128235041 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128235041 185 01 3519 https://www.sciencedirect.com/science/book/9780128235041 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128235041 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128235041 2057 01 DE-L189 HTWK-Zugang https://www.sciencedirect.com/science/book/9780128235041 132 01 0959 00 EBooks Elsevier Engineering 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| allfields_unstemmed |
0128236507 electronic publication 0-12-823650-7 9780128236505 electronic bk. 978-0-12-823650-5 (DE-627)1782505687 (DE-599)KEP069017158 (ELSEVIER)on1267766379 (EBP)069017158 DE-627 ger DE-627 rakwb eng XD-US RC78.7.D53 616.0754 23 Santosh, K. C. verfasserin aut Deep Learning Models for Medical Imaging San Diego Elsevier Science & Technology 2021 1 Online-Ressource (172 p) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Primers in Biomedical Imaging Devices and Systems Ser Description based upon print version of record 4.7.3.2 Results with data augmentation Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evolution of machine learning -- 1.3.1 Rule-based learning -- 1.3.2 Feature-based learning -- 1.3.3 Representation learning -- 1.4 Basics to deep learning -- 1.4.1 The rise of cybernetics -- 1.4.2 The connectionist movement -- 1.4.3 The onset of deep learning -- 1.4.4 Motivation: deep learning 1.5 Importance of deep learning -- 1.6 Deep learning in medical imaging: a review -- 1.6.1 Medical imaging scope -- 1.6.2 Medical imaging data -- 1.6.3 Applications: deep learning in medical imaging -- 1.7 Scope of the book -- References -- 2 Deep learning: a review -- 2.1 Background -- 2.2 Artificial neural networks -- 2.2.1 The neuron -- 2.2.2 Activation functions -- 2.2.3 Multilayer feed forward neural network -- 2.2.4 Training neural networks by back-propagation -- 2.2.5 Optimization -- 2.2.5.1 Objective functions -- Mean squared error -- Cross-entropy measures 2.2.5.2 Optimization techniques -- Stochastic gradient descent -- Momentum -- Adaptive learning rates -- 2.2.6 Regularization -- 2.3 Convolutional neural networks -- 2.3.1 Feature extraction using convolutions -- 2.3.2 Subsampling -- 2.3.3 Effect of nonlinearity on activation maps -- 2.3.4 Layer design -- 2.3.5 Output layer -- 2.4 Encoder-decoder architecture -- 2.4.1 Unsupervised learning in CNNs -- 2.4.2 Image-to-image translation -- 2.4.3 Localization -- 2.4.4 Multiscale feature propagation -- References -- 3 Deep learning models -- 3.1 Deep learning models 3.1.1 Learning different objectives -- 3.1.2 Network structure for CNNs -- 3.1.3 Types of models based on learning strategies -- 3.2 Elements in deep learning pipeline -- 3.2.1 Data preprocessing -- 3.2.2 Model selection -- 3.2.3 Model validation and hyperparameter tuning -- 3.3 Evolution of deep learning models and applications -- 3.3.1 Classification -- 3.3.2 Localization -- 3.3.3 Segmentation -- References -- 4 Cytology image analysis -- 4.1 Background -- 4.2 Cytology: a brief overview -- 4.3 Types of cytology -- 4.4 Cytology slide preparation -- 4.4.1 Aspiration cytology 4.4.2 Exfoliative cytology -- 4.4.3 Abrasive cytology -- 4.4.4 Specimen collection -- 4.4.5 Slide preparation -- 4.4.6 Fixation techniques and staining protocol -- 4.5 Cytological process and digitization -- 4.6 Cervical cell cytology -- 4.6.1 Modalities of cervical specimen collection -- 4.6.2 Characteristics of cytomorphology of malignant cells -- 4.7 Experiments -- 4.7.1 Dataset -- 4.7.2 Experimental setup and protocols -- 4.7.2.1 Transfer learning: a quick overview -- 4.7.3 Results and discussion -- 4.7.3.1 Results with or without using transfer learning Diagnostic imaging Artificial intelligence Medical applications Deep learning (Machine learning) Diagnostic Imaging (DNLM)D003952 Deep Learning (DNLM)D000077321 Imagerie pour le diagnostic (CaQQLa)201-0146124 Intelligence artificielle en medecine (CaQQLa)201-0180593 Apprentissage profond (CaQQLa)000311746 Artificial intelligence - Medical applications (OCoLC)fst00817267 Diagnostic imaging (OCoLC)fst00892354 Electronic books Das, Nibaran oth Ghosh, Swarnendu oth 9780128236505 0128236507 9780128235041 Erscheint auch als Druck-Ausgabe Santosh, K. C Deep Learning Models for Medical Imaging San Diego : Elsevier Science & Technology,c2021 9780128235041 https://www.sciencedirect.com/science/book/9780128235041 X:ELSEVIER Verlag lizenzpflichtig https://www.gbv.de/dms/bowker/toc/9780128235041.pdf 2022-07-28 Aggregator Inhaltsverzeichnis BSZ-33-ESD-L1FH GBV-33-Freedom 2021 GBV-33-Freedom 2022 ZDB-33-EBS ZDB-33-EGE 2021 GBV-33-Freedom 2023 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_60 ISIL_DE-705 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2057 ISIL_DE-L189 BO 045F 616.0754 60 01 0705 4236042169 00 --%%-- --%%-- s --%%-- Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung z 20-12-22 132 01 0959 4500007792 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514762105 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540285246 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 452034056X 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2057 01 DE-L189 4467496580 00 --%%-- --%%-- --%%-- n Campuslizenz l01 24-01-24 60 01 0705 https://www.sciencedirect.com/science/book/9780128235041 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128235041 185 01 3519 https://www.sciencedirect.com/science/book/9780128235041 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128235041 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128235041 2057 01 DE-L189 HTWK-Zugang https://www.sciencedirect.com/science/book/9780128235041 132 01 0959 00 EBooks Elsevier Engineering 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| allfieldsGer |
0128236507 electronic publication 0-12-823650-7 9780128236505 electronic bk. 978-0-12-823650-5 (DE-627)1782505687 (DE-599)KEP069017158 (ELSEVIER)on1267766379 (EBP)069017158 DE-627 ger DE-627 rakwb eng XD-US RC78.7.D53 616.0754 23 Santosh, K. C. verfasserin aut Deep Learning Models for Medical Imaging San Diego Elsevier Science & Technology 2021 1 Online-Ressource (172 p) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Primers in Biomedical Imaging Devices and Systems Ser Description based upon print version of record 4.7.3.2 Results with data augmentation Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evolution of machine learning -- 1.3.1 Rule-based learning -- 1.3.2 Feature-based learning -- 1.3.3 Representation learning -- 1.4 Basics to deep learning -- 1.4.1 The rise of cybernetics -- 1.4.2 The connectionist movement -- 1.4.3 The onset of deep learning -- 1.4.4 Motivation: deep learning 1.5 Importance of deep learning -- 1.6 Deep learning in medical imaging: a review -- 1.6.1 Medical imaging scope -- 1.6.2 Medical imaging data -- 1.6.3 Applications: deep learning in medical imaging -- 1.7 Scope of the book -- References -- 2 Deep learning: a review -- 2.1 Background -- 2.2 Artificial neural networks -- 2.2.1 The neuron -- 2.2.2 Activation functions -- 2.2.3 Multilayer feed forward neural network -- 2.2.4 Training neural networks by back-propagation -- 2.2.5 Optimization -- 2.2.5.1 Objective functions -- Mean squared error -- Cross-entropy measures 2.2.5.2 Optimization techniques -- Stochastic gradient descent -- Momentum -- Adaptive learning rates -- 2.2.6 Regularization -- 2.3 Convolutional neural networks -- 2.3.1 Feature extraction using convolutions -- 2.3.2 Subsampling -- 2.3.3 Effect of nonlinearity on activation maps -- 2.3.4 Layer design -- 2.3.5 Output layer -- 2.4 Encoder-decoder architecture -- 2.4.1 Unsupervised learning in CNNs -- 2.4.2 Image-to-image translation -- 2.4.3 Localization -- 2.4.4 Multiscale feature propagation -- References -- 3 Deep learning models -- 3.1 Deep learning models 3.1.1 Learning different objectives -- 3.1.2 Network structure for CNNs -- 3.1.3 Types of models based on learning strategies -- 3.2 Elements in deep learning pipeline -- 3.2.1 Data preprocessing -- 3.2.2 Model selection -- 3.2.3 Model validation and hyperparameter tuning -- 3.3 Evolution of deep learning models and applications -- 3.3.1 Classification -- 3.3.2 Localization -- 3.3.3 Segmentation -- References -- 4 Cytology image analysis -- 4.1 Background -- 4.2 Cytology: a brief overview -- 4.3 Types of cytology -- 4.4 Cytology slide preparation -- 4.4.1 Aspiration cytology 4.4.2 Exfoliative cytology -- 4.4.3 Abrasive cytology -- 4.4.4 Specimen collection -- 4.4.5 Slide preparation -- 4.4.6 Fixation techniques and staining protocol -- 4.5 Cytological process and digitization -- 4.6 Cervical cell cytology -- 4.6.1 Modalities of cervical specimen collection -- 4.6.2 Characteristics of cytomorphology of malignant cells -- 4.7 Experiments -- 4.7.1 Dataset -- 4.7.2 Experimental setup and protocols -- 4.7.2.1 Transfer learning: a quick overview -- 4.7.3 Results and discussion -- 4.7.3.1 Results with or without using transfer learning Diagnostic imaging Artificial intelligence Medical applications Deep learning (Machine learning) Diagnostic Imaging (DNLM)D003952 Deep Learning (DNLM)D000077321 Imagerie pour le diagnostic (CaQQLa)201-0146124 Intelligence artificielle en medecine (CaQQLa)201-0180593 Apprentissage profond (CaQQLa)000311746 Artificial intelligence - Medical applications (OCoLC)fst00817267 Diagnostic imaging (OCoLC)fst00892354 Electronic books Das, Nibaran oth Ghosh, Swarnendu oth 9780128236505 0128236507 9780128235041 Erscheint auch als Druck-Ausgabe Santosh, K. C Deep Learning Models for Medical Imaging San Diego : Elsevier Science & Technology,c2021 9780128235041 https://www.sciencedirect.com/science/book/9780128235041 X:ELSEVIER Verlag lizenzpflichtig https://www.gbv.de/dms/bowker/toc/9780128235041.pdf 2022-07-28 Aggregator Inhaltsverzeichnis BSZ-33-ESD-L1FH GBV-33-Freedom 2021 GBV-33-Freedom 2022 ZDB-33-EBS ZDB-33-EGE 2021 GBV-33-Freedom 2023 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_60 ISIL_DE-705 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2057 ISIL_DE-L189 BO 045F 616.0754 60 01 0705 4236042169 00 --%%-- --%%-- s --%%-- Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung z 20-12-22 132 01 0959 4500007792 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514762105 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540285246 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 452034056X 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2057 01 DE-L189 4467496580 00 --%%-- --%%-- --%%-- n Campuslizenz l01 24-01-24 60 01 0705 https://www.sciencedirect.com/science/book/9780128235041 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128235041 185 01 3519 https://www.sciencedirect.com/science/book/9780128235041 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128235041 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128235041 2057 01 DE-L189 HTWK-Zugang https://www.sciencedirect.com/science/book/9780128235041 132 01 0959 00 EBooks Elsevier Engineering 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| allfieldsSound |
0128236507 electronic publication 0-12-823650-7 9780128236505 electronic bk. 978-0-12-823650-5 (DE-627)1782505687 (DE-599)KEP069017158 (ELSEVIER)on1267766379 (EBP)069017158 DE-627 ger DE-627 rakwb eng XD-US RC78.7.D53 616.0754 23 Santosh, K. C. verfasserin aut Deep Learning Models for Medical Imaging San Diego Elsevier Science & Technology 2021 1 Online-Ressource (172 p) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Primers in Biomedical Imaging Devices and Systems Ser Description based upon print version of record 4.7.3.2 Results with data augmentation Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evolution of machine learning -- 1.3.1 Rule-based learning -- 1.3.2 Feature-based learning -- 1.3.3 Representation learning -- 1.4 Basics to deep learning -- 1.4.1 The rise of cybernetics -- 1.4.2 The connectionist movement -- 1.4.3 The onset of deep learning -- 1.4.4 Motivation: deep learning 1.5 Importance of deep learning -- 1.6 Deep learning in medical imaging: a review -- 1.6.1 Medical imaging scope -- 1.6.2 Medical imaging data -- 1.6.3 Applications: deep learning in medical imaging -- 1.7 Scope of the book -- References -- 2 Deep learning: a review -- 2.1 Background -- 2.2 Artificial neural networks -- 2.2.1 The neuron -- 2.2.2 Activation functions -- 2.2.3 Multilayer feed forward neural network -- 2.2.4 Training neural networks by back-propagation -- 2.2.5 Optimization -- 2.2.5.1 Objective functions -- Mean squared error -- Cross-entropy measures 2.2.5.2 Optimization techniques -- Stochastic gradient descent -- Momentum -- Adaptive learning rates -- 2.2.6 Regularization -- 2.3 Convolutional neural networks -- 2.3.1 Feature extraction using convolutions -- 2.3.2 Subsampling -- 2.3.3 Effect of nonlinearity on activation maps -- 2.3.4 Layer design -- 2.3.5 Output layer -- 2.4 Encoder-decoder architecture -- 2.4.1 Unsupervised learning in CNNs -- 2.4.2 Image-to-image translation -- 2.4.3 Localization -- 2.4.4 Multiscale feature propagation -- References -- 3 Deep learning models -- 3.1 Deep learning models 3.1.1 Learning different objectives -- 3.1.2 Network structure for CNNs -- 3.1.3 Types of models based on learning strategies -- 3.2 Elements in deep learning pipeline -- 3.2.1 Data preprocessing -- 3.2.2 Model selection -- 3.2.3 Model validation and hyperparameter tuning -- 3.3 Evolution of deep learning models and applications -- 3.3.1 Classification -- 3.3.2 Localization -- 3.3.3 Segmentation -- References -- 4 Cytology image analysis -- 4.1 Background -- 4.2 Cytology: a brief overview -- 4.3 Types of cytology -- 4.4 Cytology slide preparation -- 4.4.1 Aspiration cytology 4.4.2 Exfoliative cytology -- 4.4.3 Abrasive cytology -- 4.4.4 Specimen collection -- 4.4.5 Slide preparation -- 4.4.6 Fixation techniques and staining protocol -- 4.5 Cytological process and digitization -- 4.6 Cervical cell cytology -- 4.6.1 Modalities of cervical specimen collection -- 4.6.2 Characteristics of cytomorphology of malignant cells -- 4.7 Experiments -- 4.7.1 Dataset -- 4.7.2 Experimental setup and protocols -- 4.7.2.1 Transfer learning: a quick overview -- 4.7.3 Results and discussion -- 4.7.3.1 Results with or without using transfer learning Diagnostic imaging Artificial intelligence Medical applications Deep learning (Machine learning) Diagnostic Imaging (DNLM)D003952 Deep Learning (DNLM)D000077321 Imagerie pour le diagnostic (CaQQLa)201-0146124 Intelligence artificielle en medecine (CaQQLa)201-0180593 Apprentissage profond (CaQQLa)000311746 Artificial intelligence - Medical applications (OCoLC)fst00817267 Diagnostic imaging (OCoLC)fst00892354 Electronic books Das, Nibaran oth Ghosh, Swarnendu oth 9780128236505 0128236507 9780128235041 Erscheint auch als Druck-Ausgabe Santosh, K. C Deep Learning Models for Medical Imaging San Diego : Elsevier Science & Technology,c2021 9780128235041 https://www.sciencedirect.com/science/book/9780128235041 X:ELSEVIER Verlag lizenzpflichtig https://www.gbv.de/dms/bowker/toc/9780128235041.pdf 2022-07-28 Aggregator Inhaltsverzeichnis BSZ-33-ESD-L1FH GBV-33-Freedom 2021 GBV-33-Freedom 2022 ZDB-33-EBS ZDB-33-EGE 2021 GBV-33-Freedom 2023 ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_60 ISIL_DE-705 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2057 ISIL_DE-L189 BO 045F 616.0754 60 01 0705 4236042169 00 --%%-- --%%-- s --%%-- Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung z 20-12-22 132 01 0959 4500007792 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. Zeitlich begrenzte Lizenzierung k 13-03-24 185 01 3519 4514762105 OLR-EBS Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. z 23-04-24 370 01 4370 4540285246 EBS Elsevier Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. i z 20-06-24 2020 01 DE-Ch1 452034056X 00 --%%-- --%%-- n n Campuslizenz l01 03-05-24 2057 01 DE-L189 4467496580 00 --%%-- --%%-- --%%-- n Campuslizenz l01 24-01-24 60 01 0705 https://www.sciencedirect.com/science/book/9780128235041 132 01 0959 Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz https://www.sciencedirect.com/science/book/9780128235041 185 01 3519 https://www.sciencedirect.com/science/book/9780128235041 370 01 4370 E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich https://www.sciencedirect.com/science/book/9780128235041 2020 01 DE-Ch1 https://www.sciencedirect.com/science/book/9780128235041 2057 01 DE-L189 HTWK-Zugang https://www.sciencedirect.com/science/book/9780128235041 132 01 0959 00 EBooks Elsevier Engineering 132 01 0959 EBS Elsevier 185 01 3519 OLR-EBS 370 01 4370 EBS Elsevier |
| language |
English |
| format_phy_str_mv |
Book |
| building |
60 132 185 370 2020:0 2057:0 |
| institution |
findex.gbv.de |
| selectbib_iln_str_mv |
60@ 132@ 185@ 370@ 2020@01 2057@01 |
| topic_facet |
Diagnostic imaging Artificial intelligence Deep learning (Machine learning) Diagnostic Imaging Deep Learning Imagerie pour le diagnostic Intelligence artificielle en medecine Apprentissage profond Artificial intelligence - Medical applications Medical applications |
| sw_local_iln_str_mv |
132:EBooks Elsevier Engineering 0959:EBooks Elsevier Engineering |
| dewey-raw |
616.0754 |
| isfreeaccess_bool |
false |
| authorswithroles_txt_mv |
Santosh, K. C. @@aut@@ Das, Nibaran @@oth@@ Ghosh, Swarnendu @@oth@@ |
| publishDateDaySort_date |
2021-01-01T00:00:00Z |
| dewey-sort |
3616.0754 |
| id |
1782505687 |
| genre_facet |
Electronic books |
| language_de |
englisch |
| standort_str_mv |
--%%-- |
| callnumber-first |
R - Medicine |
| series2 |
Primers in Biomedical Imaging Devices and Systems Ser |
| standort_iln_str_mv |
60:--%%-- 0705:--%%-- 2020:--%%-- DE-Ch1:--%%-- 2057:--%%-- DE-L189:--%%-- |
| author |
Santosh, K. C. |
| spellingShingle |
Santosh, K. C. misc RC78.7.D53 ddc 616.0754 misc Diagnostic imaging misc Artificial intelligence misc Deep learning (Machine learning) misc Diagnostic Imaging misc Deep Learning misc Imagerie pour le diagnostic misc Intelligence artificielle en medecine misc Apprentissage profond misc Artificial intelligence - Medical applications 132 EBooks Elsevier Engineering Deep Learning Models for Medical Imaging |
| authorStr |
Santosh, K. C. |
| format |
eBook |
| dewey-ones |
616 - Diseases |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
KXP GVK SWB |
| publishPlace |
San Diego |
| remote_str |
true |
| abrufzeichen_iln_str_mv |
132@EBS Elsevier 185@OLR-EBS 370@EBS Elsevier |
| abrufzeichen_iln_scis_mv |
132@EBS Elsevier 185@OLR-EBS 370@EBS Elsevier |
| callnumber-label |
RC78 |
| last_changed_iln_str_mv |
60@20-12-22 132@13-03-24 185@23-04-24 370@20-06-24 2020@03-05-24 2057@24-01-24 |
| illustrated |
Not Illustrated |
| topic_title |
RC78.7.D53 616.0754 23 132 01 0959 00 EBooks Elsevier Engineering Deep Learning Models for Medical Imaging Diagnostic imaging Artificial intelligence Medical applications Deep learning (Machine learning) Diagnostic Imaging (DNLM)D003952 Deep Learning (DNLM)D000077321 Imagerie pour le diagnostic (CaQQLa)201-0146124 Intelligence artificielle en medecine (CaQQLa)201-0180593 Apprentissage profond (CaQQLa)000311746 Artificial intelligence - Medical applications (OCoLC)fst00817267 Diagnostic imaging (OCoLC)fst00892354 |
| publisher |
Elsevier Science & Technology |
| publisherStr |
Elsevier Science & Technology |
| topic |
misc RC78.7.D53 ddc 616.0754 misc Diagnostic imaging misc Artificial intelligence misc Deep learning (Machine learning) misc Diagnostic Imaging misc Deep Learning misc Imagerie pour le diagnostic misc Intelligence artificielle en medecine misc Apprentissage profond misc Artificial intelligence - Medical applications 132 EBooks Elsevier Engineering |
| topic_unstemmed |
misc RC78.7.D53 ddc 616.0754 misc Diagnostic imaging misc Artificial intelligence misc Deep learning (Machine learning) misc Diagnostic Imaging misc Deep Learning misc Imagerie pour le diagnostic misc Intelligence artificielle en medecine misc Apprentissage profond misc Artificial intelligence - Medical applications 132 EBooks Elsevier Engineering |
| topic_browse |
misc RC78.7.D53 ddc 616.0754 misc Diagnostic imaging misc Artificial intelligence misc Deep learning (Machine learning) misc Diagnostic Imaging misc Deep Learning misc Imagerie pour le diagnostic misc Intelligence artificielle en medecine misc Apprentissage profond misc Artificial intelligence - Medical applications 132 EBooks Elsevier Engineering |
| format_facet |
Elektronische Bücher Bücher Elektronische Ressource |
| standort_txtP_mv |
--%%-- |
| format_main_str_mv |
Text Buch |
| carriertype_str_mv |
cr |
| author2_variant |
n d nd s g sg |
| signature |
--%%-- |
| signature_str_mv |
--%%-- |
| dewey-tens |
610 - Medicine & health |
| isbn |
0128236507 9780128236505 9780128235041 0128235047 |
| isfreeaccess_txt |
false |
| title |
Deep Learning Models for Medical Imaging |
| ctrlnum |
(DE-627)1782505687 (DE-599)KEP069017158 (ELSEVIER)on1267766379 (EBP)069017158 |
| exemplarkommentar_str_mv |
60@Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. 60@Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung 132@Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. 132@Zeitlich begrenzte Lizenzierung 185@Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt. 370@Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots. 2020@Campuslizenz 2057@Campuslizenz |
| title_full |
Deep Learning Models for Medical Imaging |
| author_sort |
Santosh, K. C. |
| callnumber-first-code |
R |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| genre |
Electronic books |
| publishDateSort |
2021 |
| contenttype_str_mv |
txt |
| author_browse |
Santosh, K. C. |
| selectkey |
60:z 132:k 185:z 370:z 2020:l 2057:l |
| physical |
1 Online-Ressource (172 p) |
| class |
RC78.7.D53 616.0754 23 |
| format_se |
Elektronische Bücher |
| countryofpublication_str_mv |
XD-US |
| author-letter |
Santosh, K. C. |
| normlink |
(DNLM)D003952 (DNLM)D000077321 (CAQQLA)201-0146124 (CAQQLA)201-0180593 (CAQQLA)000311746 (OCOLC)FST00817267 (OCOLC)FST00892354 |
| normlink_prefix_str_mv |
(DNLM)D003952 (DNLM)D000077321 (CaQQLa)201-0146124 (CaQQLa)201-0180593 (CaQQLa)000311746 (OCoLC)fst00817267 (OCoLC)fst00892354 |
| dewey-full |
616.0754 |
| title_sort |
deep learning models for medical imaging |
| callnumber |
RC78.7.D53 |
| title_auth |
Deep Learning Models for Medical Imaging |
| abstract |
Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evolution of machine learning -- 1.3.1 Rule-based learning -- 1.3.2 Feature-based learning -- 1.3.3 Representation learning -- 1.4 Basics to deep learning -- 1.4.1 The rise of cybernetics -- 1.4.2 The connectionist movement -- 1.4.3 The onset of deep learning -- 1.4.4 Motivation: deep learning 1.5 Importance of deep learning -- 1.6 Deep learning in medical imaging: a review -- 1.6.1 Medical imaging scope -- 1.6.2 Medical imaging data -- 1.6.3 Applications: deep learning in medical imaging -- 1.7 Scope of the book -- References -- 2 Deep learning: a review -- 2.1 Background -- 2.2 Artificial neural networks -- 2.2.1 The neuron -- 2.2.2 Activation functions -- 2.2.3 Multilayer feed forward neural network -- 2.2.4 Training neural networks by back-propagation -- 2.2.5 Optimization -- 2.2.5.1 Objective functions -- Mean squared error -- Cross-entropy measures 2.2.5.2 Optimization techniques -- Stochastic gradient descent -- Momentum -- Adaptive learning rates -- 2.2.6 Regularization -- 2.3 Convolutional neural networks -- 2.3.1 Feature extraction using convolutions -- 2.3.2 Subsampling -- 2.3.3 Effect of nonlinearity on activation maps -- 2.3.4 Layer design -- 2.3.5 Output layer -- 2.4 Encoder-decoder architecture -- 2.4.1 Unsupervised learning in CNNs -- 2.4.2 Image-to-image translation -- 2.4.3 Localization -- 2.4.4 Multiscale feature propagation -- References -- 3 Deep learning models -- 3.1 Deep learning models 3.1.1 Learning different objectives -- 3.1.2 Network structure for CNNs -- 3.1.3 Types of models based on learning strategies -- 3.2 Elements in deep learning pipeline -- 3.2.1 Data preprocessing -- 3.2.2 Model selection -- 3.2.3 Model validation and hyperparameter tuning -- 3.3 Evolution of deep learning models and applications -- 3.3.1 Classification -- 3.3.2 Localization -- 3.3.3 Segmentation -- References -- 4 Cytology image analysis -- 4.1 Background -- 4.2 Cytology: a brief overview -- 4.3 Types of cytology -- 4.4 Cytology slide preparation -- 4.4.1 Aspiration cytology 4.4.2 Exfoliative cytology -- 4.4.3 Abrasive cytology -- 4.4.4 Specimen collection -- 4.4.5 Slide preparation -- 4.4.6 Fixation techniques and staining protocol -- 4.5 Cytological process and digitization -- 4.6 Cervical cell cytology -- 4.6.1 Modalities of cervical specimen collection -- 4.6.2 Characteristics of cytomorphology of malignant cells -- 4.7 Experiments -- 4.7.1 Dataset -- 4.7.2 Experimental setup and protocols -- 4.7.2.1 Transfer learning: a quick overview -- 4.7.3 Results and discussion -- 4.7.3.1 Results with or without using transfer learning Description based upon print version of record 4.7.3.2 Results with data augmentation |
| abstractGer |
Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evolution of machine learning -- 1.3.1 Rule-based learning -- 1.3.2 Feature-based learning -- 1.3.3 Representation learning -- 1.4 Basics to deep learning -- 1.4.1 The rise of cybernetics -- 1.4.2 The connectionist movement -- 1.4.3 The onset of deep learning -- 1.4.4 Motivation: deep learning 1.5 Importance of deep learning -- 1.6 Deep learning in medical imaging: a review -- 1.6.1 Medical imaging scope -- 1.6.2 Medical imaging data -- 1.6.3 Applications: deep learning in medical imaging -- 1.7 Scope of the book -- References -- 2 Deep learning: a review -- 2.1 Background -- 2.2 Artificial neural networks -- 2.2.1 The neuron -- 2.2.2 Activation functions -- 2.2.3 Multilayer feed forward neural network -- 2.2.4 Training neural networks by back-propagation -- 2.2.5 Optimization -- 2.2.5.1 Objective functions -- Mean squared error -- Cross-entropy measures 2.2.5.2 Optimization techniques -- Stochastic gradient descent -- Momentum -- Adaptive learning rates -- 2.2.6 Regularization -- 2.3 Convolutional neural networks -- 2.3.1 Feature extraction using convolutions -- 2.3.2 Subsampling -- 2.3.3 Effect of nonlinearity on activation maps -- 2.3.4 Layer design -- 2.3.5 Output layer -- 2.4 Encoder-decoder architecture -- 2.4.1 Unsupervised learning in CNNs -- 2.4.2 Image-to-image translation -- 2.4.3 Localization -- 2.4.4 Multiscale feature propagation -- References -- 3 Deep learning models -- 3.1 Deep learning models 3.1.1 Learning different objectives -- 3.1.2 Network structure for CNNs -- 3.1.3 Types of models based on learning strategies -- 3.2 Elements in deep learning pipeline -- 3.2.1 Data preprocessing -- 3.2.2 Model selection -- 3.2.3 Model validation and hyperparameter tuning -- 3.3 Evolution of deep learning models and applications -- 3.3.1 Classification -- 3.3.2 Localization -- 3.3.3 Segmentation -- References -- 4 Cytology image analysis -- 4.1 Background -- 4.2 Cytology: a brief overview -- 4.3 Types of cytology -- 4.4 Cytology slide preparation -- 4.4.1 Aspiration cytology 4.4.2 Exfoliative cytology -- 4.4.3 Abrasive cytology -- 4.4.4 Specimen collection -- 4.4.5 Slide preparation -- 4.4.6 Fixation techniques and staining protocol -- 4.5 Cytological process and digitization -- 4.6 Cervical cell cytology -- 4.6.1 Modalities of cervical specimen collection -- 4.6.2 Characteristics of cytomorphology of malignant cells -- 4.7 Experiments -- 4.7.1 Dataset -- 4.7.2 Experimental setup and protocols -- 4.7.2.1 Transfer learning: a quick overview -- 4.7.3 Results and discussion -- 4.7.3.1 Results with or without using transfer learning Description based upon print version of record 4.7.3.2 Results with data augmentation |
| abstract_unstemmed |
Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evolution of machine learning -- 1.3.1 Rule-based learning -- 1.3.2 Feature-based learning -- 1.3.3 Representation learning -- 1.4 Basics to deep learning -- 1.4.1 The rise of cybernetics -- 1.4.2 The connectionist movement -- 1.4.3 The onset of deep learning -- 1.4.4 Motivation: deep learning 1.5 Importance of deep learning -- 1.6 Deep learning in medical imaging: a review -- 1.6.1 Medical imaging scope -- 1.6.2 Medical imaging data -- 1.6.3 Applications: deep learning in medical imaging -- 1.7 Scope of the book -- References -- 2 Deep learning: a review -- 2.1 Background -- 2.2 Artificial neural networks -- 2.2.1 The neuron -- 2.2.2 Activation functions -- 2.2.3 Multilayer feed forward neural network -- 2.2.4 Training neural networks by back-propagation -- 2.2.5 Optimization -- 2.2.5.1 Objective functions -- Mean squared error -- Cross-entropy measures 2.2.5.2 Optimization techniques -- Stochastic gradient descent -- Momentum -- Adaptive learning rates -- 2.2.6 Regularization -- 2.3 Convolutional neural networks -- 2.3.1 Feature extraction using convolutions -- 2.3.2 Subsampling -- 2.3.3 Effect of nonlinearity on activation maps -- 2.3.4 Layer design -- 2.3.5 Output layer -- 2.4 Encoder-decoder architecture -- 2.4.1 Unsupervised learning in CNNs -- 2.4.2 Image-to-image translation -- 2.4.3 Localization -- 2.4.4 Multiscale feature propagation -- References -- 3 Deep learning models -- 3.1 Deep learning models 3.1.1 Learning different objectives -- 3.1.2 Network structure for CNNs -- 3.1.3 Types of models based on learning strategies -- 3.2 Elements in deep learning pipeline -- 3.2.1 Data preprocessing -- 3.2.2 Model selection -- 3.2.3 Model validation and hyperparameter tuning -- 3.3 Evolution of deep learning models and applications -- 3.3.1 Classification -- 3.3.2 Localization -- 3.3.3 Segmentation -- References -- 4 Cytology image analysis -- 4.1 Background -- 4.2 Cytology: a brief overview -- 4.3 Types of cytology -- 4.4 Cytology slide preparation -- 4.4.1 Aspiration cytology 4.4.2 Exfoliative cytology -- 4.4.3 Abrasive cytology -- 4.4.4 Specimen collection -- 4.4.5 Slide preparation -- 4.4.6 Fixation techniques and staining protocol -- 4.5 Cytological process and digitization -- 4.6 Cervical cell cytology -- 4.6.1 Modalities of cervical specimen collection -- 4.6.2 Characteristics of cytomorphology of malignant cells -- 4.7 Experiments -- 4.7.1 Dataset -- 4.7.2 Experimental setup and protocols -- 4.7.2.1 Transfer learning: a quick overview -- 4.7.3 Results and discussion -- 4.7.3.1 Results with or without using transfer learning Description based upon print version of record 4.7.3.2 Results with data augmentation |
| collection_details |
BSZ-33-ESD-L1FH GBV-33-Freedom ZDB-33-EBS ZDB-33-EGE ZDB-33-ESD GBV-33-EBS-HST BSZ-33-EBS-C1UB GBV_ILN_60 ISIL_DE-705 SYSFLAG_1 GBV_KXP GBV_ILN_132 ISIL_DE-959 GBV_ILN_185 ISIL_DE-Sra5 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2020 ISIL_DE-Ch1 GBV_ILN_2057 ISIL_DE-L189 |
| title_short |
Deep Learning Models for Medical Imaging |
| url |
https://www.sciencedirect.com/science/book/9780128235041 https://www.gbv.de/dms/bowker/toc/9780128235041.pdf |
| ausleihindikator_str_mv |
60:s 132 185 370 2020:n 2057:- |
| remote_bool |
true |
| author2 |
Das, Nibaran Ghosh, Swarnendu |
| author2Str |
Das, Nibaran Ghosh, Swarnendu |
| callnumber-subject |
RC - Internal Medicine |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth |
| callnumber-a |
RC78.7.D53 |
| up_date |
2024-07-17T11:57:38.743Z |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000cam a22002652 4500</leader><controlfield tag="001">1782505687</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240503140114.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">211217s2021 xxu|||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0128236507</subfield><subfield code="c">electronic publication</subfield><subfield code="9">0-12-823650-7</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780128236505</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">978-0-12-823650-5</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1782505687</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP069017158</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)on1267766379</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EBP)069017158</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XD-US</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC78.7.D53</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">616.0754</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Santosh, K. C.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Deep Learning Models for Medical Imaging</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">San Diego</subfield><subfield code="b">Elsevier Science & Technology</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (172 p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Primers in Biomedical Imaging Devices and Systems Ser</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based upon print version of record</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">4.7.3.2 Results with data augmentation</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evolution of machine learning -- 1.3.1 Rule-based learning -- 1.3.2 Feature-based learning -- 1.3.3 Representation learning -- 1.4 Basics to deep learning -- 1.4.1 The rise of cybernetics -- 1.4.2 The connectionist movement -- 1.4.3 The onset of deep learning -- 1.4.4 Motivation: deep learning</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">1.5 Importance of deep learning -- 1.6 Deep learning in medical imaging: a review -- 1.6.1 Medical imaging scope -- 1.6.2 Medical imaging data -- 1.6.3 Applications: deep learning in medical imaging -- 1.7 Scope of the book -- References -- 2 Deep learning: a review -- 2.1 Background -- 2.2 Artificial neural networks -- 2.2.1 The neuron -- 2.2.2 Activation functions -- 2.2.3 Multilayer feed forward neural network -- 2.2.4 Training neural networks by back-propagation -- 2.2.5 Optimization -- 2.2.5.1 Objective functions -- Mean squared error -- Cross-entropy measures</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">2.2.5.2 Optimization techniques -- Stochastic gradient descent -- Momentum -- Adaptive learning rates -- 2.2.6 Regularization -- 2.3 Convolutional neural networks -- 2.3.1 Feature extraction using convolutions -- 2.3.2 Subsampling -- 2.3.3 Effect of nonlinearity on activation maps -- 2.3.4 Layer design -- 2.3.5 Output layer -- 2.4 Encoder-decoder architecture -- 2.4.1 Unsupervised learning in CNNs -- 2.4.2 Image-to-image translation -- 2.4.3 Localization -- 2.4.4 Multiscale feature propagation -- References -- 3 Deep learning models -- 3.1 Deep learning models</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">3.1.1 Learning different objectives -- 3.1.2 Network structure for CNNs -- 3.1.3 Types of models based on learning strategies -- 3.2 Elements in deep learning pipeline -- 3.2.1 Data preprocessing -- 3.2.2 Model selection -- 3.2.3 Model validation and hyperparameter tuning -- 3.3 Evolution of deep learning models and applications -- 3.3.1 Classification -- 3.3.2 Localization -- 3.3.3 Segmentation -- References -- 4 Cytology image analysis -- 4.1 Background -- 4.2 Cytology: a brief overview -- 4.3 Types of cytology -- 4.4 Cytology slide preparation -- 4.4.1 Aspiration cytology</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">4.4.2 Exfoliative cytology -- 4.4.3 Abrasive cytology -- 4.4.4 Specimen collection -- 4.4.5 Slide preparation -- 4.4.6 Fixation techniques and staining protocol -- 4.5 Cytological process and digitization -- 4.6 Cervical cell cytology -- 4.6.1 Modalities of cervical specimen collection -- 4.6.2 Characteristics of cytomorphology of malignant cells -- 4.7 Experiments -- 4.7.1 Dataset -- 4.7.2 Experimental setup and protocols -- 4.7.2.1 Transfer learning: a quick overview -- 4.7.3 Results and discussion -- 4.7.3.1 Results with or without using transfer learning</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Diagnostic imaging</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Artificial intelligence</subfield><subfield code="x">Medical applications</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Deep learning (Machine learning)</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Diagnostic Imaging</subfield><subfield code="0">(DNLM)D003952</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Deep Learning</subfield><subfield code="0">(DNLM)D000077321</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Imagerie pour le diagnostic</subfield><subfield code="0">(CaQQLa)201-0146124</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intelligence artificielle en medecine</subfield><subfield code="0">(CaQQLa)201-0180593</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage profond</subfield><subfield code="0">(CaQQLa)000311746</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence - Medical applications</subfield><subfield code="0">(OCoLC)fst00817267</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diagnostic imaging</subfield><subfield code="0">(OCoLC)fst00892354</subfield></datafield><datafield tag="655" ind1=" " ind2="0"><subfield code="a">Electronic books</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Das, Nibaran</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ghosh, Swarnendu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">9780128236505</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">0128236507</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">9780128235041</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Santosh, K. C</subfield><subfield code="t">Deep Learning Models for Medical Imaging</subfield><subfield code="d">San Diego : Elsevier Science & Technology,c2021</subfield><subfield code="z">9780128235041</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.sciencedirect.com/science/book/9780128235041</subfield><subfield code="m">X:ELSEVIER</subfield><subfield code="x">Verlag</subfield><subfield code="z">lizenzpflichtig</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://www.gbv.de/dms/bowker/toc/9780128235041.pdf</subfield><subfield code="v">2022-07-28</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">BSZ-33-ESD-L1FH</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-33-Freedom</subfield><subfield code="b">2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-33-Freedom</subfield><subfield code="b">2022</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-33-EBS</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-33-EGE</subfield><subfield code="b">2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-33-Freedom</subfield><subfield code="b">2023</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-33-ESD</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-33-EBS-HST</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">BSZ-33-EBS-C1UB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-705</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_132</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-959</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_185</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Sra5</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-1373</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Ch1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-L189</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">616.0754</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="b">4236042169</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">s</subfield><subfield code="j">--%%--</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots.</subfield><subfield code="k">Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung</subfield><subfield code="y">z</subfield><subfield code="z">20-12-22</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="b">4500007792</subfield><subfield code="h">EBS Elsevier</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots.</subfield><subfield code="k">Zeitlich begrenzte Lizenzierung</subfield><subfield code="y">k</subfield><subfield code="z">13-03-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">185</subfield><subfield code="1">01</subfield><subfield code="x">3519</subfield><subfield code="b">4514762105</subfield><subfield code="h">OLR-EBS</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">23-04-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="b">4540285246</subfield><subfield code="h">EBS Elsevier</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots.</subfield><subfield code="u">i</subfield><subfield code="y">z</subfield><subfield code="z">20-06-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2020</subfield><subfield code="1">01</subfield><subfield code="x">DE-Ch1</subfield><subfield code="b">452034056X</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">n</subfield><subfield code="k">Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">03-05-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2057</subfield><subfield code="1">01</subfield><subfield code="x">DE-L189</subfield><subfield code="b">4467496580</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">24-01-24</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="r">https://www.sciencedirect.com/science/book/9780128235041</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="y">Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz</subfield><subfield code="r">https://www.sciencedirect.com/science/book/9780128235041</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">185</subfield><subfield code="1">01</subfield><subfield code="x">3519</subfield><subfield code="r">https://www.sciencedirect.com/science/book/9780128235041</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="y">E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich</subfield><subfield code="r">https://www.sciencedirect.com/science/book/9780128235041</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2020</subfield><subfield code="1">01</subfield><subfield code="x">DE-Ch1</subfield><subfield code="r">https://www.sciencedirect.com/science/book/9780128235041</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2057</subfield><subfield code="1">01</subfield><subfield code="x">DE-L189</subfield><subfield code="y">HTWK-Zugang</subfield><subfield code="r">https://www.sciencedirect.com/science/book/9780128235041</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="8">00</subfield><subfield code="a">EBooks Elsevier Engineering</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="a">EBS Elsevier</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">185</subfield><subfield code="1">01</subfield><subfield code="x">3519</subfield><subfield code="a">OLR-EBS</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="a">EBS Elsevier</subfield></datafield></record></collection>
|
| fulltext |
List of figures p. ix List of tables p. xiii Authors p. xv KC Santosh p. xv Nibaran Das p. xv Swarnendu Ghosh p. xvi Foreword p. xvii Preface p. xix Acronyms p. xxi Introduction … Background … Machine learning and its types … Evolution of machine learning … Rule-based learning … Feature-based learning … Representation learning … Basics to deep learning … The rise of cybernetics … The connectionist movement … The onset of deep learning … Motivation: deep learning … Importance of deep learning … Deep learning in medical imaging: a review … Medical imaging scope … Medical imaging data … Applications: deep learning in medical imaging … Scope of the book … References … Deep learning: a review … Background … Artificial neural networks … The neuron … Activation functions … Multilayer feed forward neural network … Training neural networks by back-propagation … Optimization … Regularization … Convolutional neural networks … Feature extraction using convolutions … Subsampling … Effect of nonlinearity on activation maps … Layer design … Output layer … Encoder-decoder architecture … Unsupervised learning in CNNs … Image-to-image translation … Localization … Multiscale feature propagation … References … Deep learning models … Deep learning models … Learning different objectives … Network structure for CNNs … Types of models based on learning strategies … Elements in deep learning pipeline … Data preprocessing … Model selection … Model validation and hyperparameter tuning … Evolution of deep learning models and applications … Classification … Localization … Segmentation … References … Cytology image analysis … Background … Cytology: a brief overview … Types of cytology … Cytology slide preparation … Aspiration cytology … Exfoliative cytology … Abrasive cytology … Specimen collection … Slide preparation … Fixation techniques and staining protocol … Cytological process and digitization … Cervical cell cytology … Modalities of cervical specimen collection … Characteristics of cytomorphology of malignant cells … Experiments … Dataset … Experimental setup and protocols … Results and discussion … Summary … References … Covid- … prediction, screening, and decision-making … Background … Predictive modeling and infectious disease outbreaks … Need of medical imaging tools for Covid- … outbreak screening … Deep neural networks for Covid- … screening … Truncated Inception Net: Covid- … outbreak screening using chest X-rays … Shallow CNN for Covid- … outbreak screening using chest X-rays … DNN to detect Covid- … one architecture for both chest CT and X-ray images … Discussion: how big data is big? … References … Index … |
| _version_ |
1804827318022045696 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000cam a22002652 4500</leader><controlfield tag="001">1782505687</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240503140114.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">211217s2021 xxu|||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0128236507</subfield><subfield code="c">electronic publication</subfield><subfield code="9">0-12-823650-7</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780128236505</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">978-0-12-823650-5</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1782505687</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP069017158</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)on1267766379</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EBP)069017158</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="c">XD-US</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">RC78.7.D53</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">616.0754</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Santosh, K. C.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Deep Learning Models for Medical Imaging</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">San Diego</subfield><subfield code="b">Elsevier Science & Technology</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (172 p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Primers in Biomedical Imaging Devices and Systems Ser</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based upon print version of record</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">4.7.3.2 Results with data augmentation</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Front Cover -- Deep Learning Models for Medical Imaging -- Copyright -- Contents -- List of figures -- List of tables -- Authors -- KC Santosh -- Nibaran Das -- Swarnendu Ghosh -- Foreword -- Preface -- Acronyms -- 1 Introduction -- 1.1 Background -- 1.2 Machine learning and its types -- 1.3 Evolution of machine learning -- 1.3.1 Rule-based learning -- 1.3.2 Feature-based learning -- 1.3.3 Representation learning -- 1.4 Basics to deep learning -- 1.4.1 The rise of cybernetics -- 1.4.2 The connectionist movement -- 1.4.3 The onset of deep learning -- 1.4.4 Motivation: deep learning</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">1.5 Importance of deep learning -- 1.6 Deep learning in medical imaging: a review -- 1.6.1 Medical imaging scope -- 1.6.2 Medical imaging data -- 1.6.3 Applications: deep learning in medical imaging -- 1.7 Scope of the book -- References -- 2 Deep learning: a review -- 2.1 Background -- 2.2 Artificial neural networks -- 2.2.1 The neuron -- 2.2.2 Activation functions -- 2.2.3 Multilayer feed forward neural network -- 2.2.4 Training neural networks by back-propagation -- 2.2.5 Optimization -- 2.2.5.1 Objective functions -- Mean squared error -- Cross-entropy measures</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">2.2.5.2 Optimization techniques -- Stochastic gradient descent -- Momentum -- Adaptive learning rates -- 2.2.6 Regularization -- 2.3 Convolutional neural networks -- 2.3.1 Feature extraction using convolutions -- 2.3.2 Subsampling -- 2.3.3 Effect of nonlinearity on activation maps -- 2.3.4 Layer design -- 2.3.5 Output layer -- 2.4 Encoder-decoder architecture -- 2.4.1 Unsupervised learning in CNNs -- 2.4.2 Image-to-image translation -- 2.4.3 Localization -- 2.4.4 Multiscale feature propagation -- References -- 3 Deep learning models -- 3.1 Deep learning models</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">3.1.1 Learning different objectives -- 3.1.2 Network structure for CNNs -- 3.1.3 Types of models based on learning strategies -- 3.2 Elements in deep learning pipeline -- 3.2.1 Data preprocessing -- 3.2.2 Model selection -- 3.2.3 Model validation and hyperparameter tuning -- 3.3 Evolution of deep learning models and applications -- 3.3.1 Classification -- 3.3.2 Localization -- 3.3.3 Segmentation -- References -- 4 Cytology image analysis -- 4.1 Background -- 4.2 Cytology: a brief overview -- 4.3 Types of cytology -- 4.4 Cytology slide preparation -- 4.4.1 Aspiration cytology</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">4.4.2 Exfoliative cytology -- 4.4.3 Abrasive cytology -- 4.4.4 Specimen collection -- 4.4.5 Slide preparation -- 4.4.6 Fixation techniques and staining protocol -- 4.5 Cytological process and digitization -- 4.6 Cervical cell cytology -- 4.6.1 Modalities of cervical specimen collection -- 4.6.2 Characteristics of cytomorphology of malignant cells -- 4.7 Experiments -- 4.7.1 Dataset -- 4.7.2 Experimental setup and protocols -- 4.7.2.1 Transfer learning: a quick overview -- 4.7.3 Results and discussion -- 4.7.3.1 Results with or without using transfer learning</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Diagnostic imaging</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Artificial intelligence</subfield><subfield code="x">Medical applications</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Deep learning (Machine learning)</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Diagnostic Imaging</subfield><subfield code="0">(DNLM)D003952</subfield></datafield><datafield tag="650" ind1=" " ind2="2"><subfield code="a">Deep Learning</subfield><subfield code="0">(DNLM)D000077321</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Imagerie pour le diagnostic</subfield><subfield code="0">(CaQQLa)201-0146124</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intelligence artificielle en medecine</subfield><subfield code="0">(CaQQLa)201-0180593</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Apprentissage profond</subfield><subfield code="0">(CaQQLa)000311746</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence - Medical applications</subfield><subfield code="0">(OCoLC)fst00817267</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diagnostic imaging</subfield><subfield code="0">(OCoLC)fst00892354</subfield></datafield><datafield tag="655" ind1=" " ind2="0"><subfield code="a">Electronic books</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Das, Nibaran</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ghosh, Swarnendu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">9780128236505</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">0128236507</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">9780128235041</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Santosh, K. C</subfield><subfield code="t">Deep Learning Models for Medical Imaging</subfield><subfield code="d">San Diego : Elsevier Science & Technology,c2021</subfield><subfield code="z">9780128235041</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://www.sciencedirect.com/science/book/9780128235041</subfield><subfield code="m">X:ELSEVIER</subfield><subfield code="x">Verlag</subfield><subfield code="z">lizenzpflichtig</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://www.gbv.de/dms/bowker/toc/9780128235041.pdf</subfield><subfield code="v">2022-07-28</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">BSZ-33-ESD-L1FH</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-33-Freedom</subfield><subfield code="b">2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-33-Freedom</subfield><subfield code="b">2022</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-33-EBS</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-33-EGE</subfield><subfield code="b">2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-33-Freedom</subfield><subfield code="b">2023</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-33-ESD</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV-33-EBS-HST</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">BSZ-33-EBS-C1UB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-705</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_132</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-959</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_185</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Sra5</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-1373</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-Ch1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2057</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-L189</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">616.0754</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="b">4236042169</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">s</subfield><subfield code="j">--%%--</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots.</subfield><subfield code="k">Nur für Angehörige der HSU: Volltextzugang von außerhalb des Campus mit Anmeldung über Shibboleth mit Ihrer Bibliothekskennung</subfield><subfield code="y">z</subfield><subfield code="z">20-12-22</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="b">4500007792</subfield><subfield code="h">EBS Elsevier</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots.</subfield><subfield code="k">Zeitlich begrenzte Lizenzierung</subfield><subfield code="y">k</subfield><subfield code="z">13-03-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">185</subfield><subfield code="1">01</subfield><subfield code="x">3519</subfield><subfield code="b">4514762105</subfield><subfield code="h">OLR-EBS</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Die Weitergabe an Dritte sowie systematisches Downloaden sind untersagt.</subfield><subfield code="y">z</subfield><subfield code="z">23-04-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="b">4540285246</subfield><subfield code="h">EBS Elsevier</subfield><subfield code="k">Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots.</subfield><subfield code="u">i</subfield><subfield code="y">z</subfield><subfield code="z">20-06-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2020</subfield><subfield code="1">01</subfield><subfield code="x">DE-Ch1</subfield><subfield code="b">452034056X</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">n</subfield><subfield code="k">Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">03-05-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2057</subfield><subfield code="1">01</subfield><subfield code="x">DE-L189</subfield><subfield code="b">4467496580</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">--%%--</subfield><subfield code="j">n</subfield><subfield code="k">Campuslizenz</subfield><subfield code="y">l01</subfield><subfield code="z">24-01-24</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">60</subfield><subfield code="1">01</subfield><subfield code="x">0705</subfield><subfield code="r">https://www.sciencedirect.com/science/book/9780128235041</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="y">Zugriff nur für Angehörige der Hochschule Osnabrück im Hochschulnetz</subfield><subfield code="r">https://www.sciencedirect.com/science/book/9780128235041</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">185</subfield><subfield code="1">01</subfield><subfield code="x">3519</subfield><subfield code="r">https://www.sciencedirect.com/science/book/9780128235041</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="y">E-Book: Zugriff im HCU-Netz. Zugriff von außerhalb nur für HCU-Angehörige möglich</subfield><subfield code="r">https://www.sciencedirect.com/science/book/9780128235041</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2020</subfield><subfield code="1">01</subfield><subfield code="x">DE-Ch1</subfield><subfield code="r">https://www.sciencedirect.com/science/book/9780128235041</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2057</subfield><subfield code="1">01</subfield><subfield code="x">DE-L189</subfield><subfield code="y">HTWK-Zugang</subfield><subfield code="r">https://www.sciencedirect.com/science/book/9780128235041</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="8">00</subfield><subfield code="a">EBooks Elsevier Engineering</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">132</subfield><subfield code="1">01</subfield><subfield code="x">0959</subfield><subfield code="a">EBS Elsevier</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">185</subfield><subfield code="1">01</subfield><subfield code="x">3519</subfield><subfield code="a">OLR-EBS</subfield></datafield><datafield tag="995" ind1=" " ind2=" "><subfield code="2">370</subfield><subfield code="1">01</subfield><subfield code="x">4370</subfield><subfield code="a">EBS Elsevier</subfield></datafield></record></collection>
|
| score |
7.1657686 |