Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer

BackgroundUltrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Qingmin Wang [verfasserIn] Xiaohong Jia [verfasserIn] Ting Luo [verfasserIn] Jinhua Yu [verfasserIn] Shujun Xia [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	ultrasound radiofrequency signals bispectrum analysis breast cancer deep learning weight sharing attention mechanism

Übergeordnetes Werk:	In: Frontiers in Oncology - Frontiers Media S.A., 2012, 13(2023)
Übergeordnetes Werk:	volume:13 ; year:2023

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fonc.2023.1272427

Katalog-ID:	DOAJ100021506

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245	1	0	\|a Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer
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520			\|a BackgroundUltrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection.MethodsIn this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance.ResultsThe experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study.ConclusionThe combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals.
650		4	\|a ultrasound radiofrequency signals
650		4	\|a bispectrum analysis
650		4	\|a breast cancer
650		4	\|a deep learning
650		4	\|a weight sharing
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653		0	\|a Neoplasms. Tumors. Oncology. Including cancer and carcinogens
700	0		\|a Xiaohong Jia \|e verfasserin \|4 aut
700	0		\|a Ting Luo \|e verfasserin \|4 aut
700	0		\|a Jinhua Yu \|e verfasserin \|4 aut
700	0		\|a Shujun Xia \|e verfasserin \|4 aut
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allfields	10.3389/fonc.2023.1272427 doi (DE-627)DOAJ100021506 (DE-599)DOAJ22d3dd3f68ea4f4a9dddea8ae53c35b0 DE-627 ger DE-627 rakwb eng RC254-282 Qingmin Wang verfasserin aut Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundUltrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection.MethodsIn this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance.ResultsThe experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study.ConclusionThe combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals. ultrasound radiofrequency signals bispectrum analysis breast cancer deep learning weight sharing attention mechanism Neoplasms. Tumors. Oncology. Including cancer and carcinogens Xiaohong Jia verfasserin aut Ting Luo verfasserin aut Jinhua Yu verfasserin aut Shujun Xia verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 13(2023) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:13 year:2023 https://doi.org/10.3389/fonc.2023.1272427 kostenfrei https://doaj.org/article/22d3dd3f68ea4f4a9dddea8ae53c35b0 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2023.1272427/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023
spelling	10.3389/fonc.2023.1272427 doi (DE-627)DOAJ100021506 (DE-599)DOAJ22d3dd3f68ea4f4a9dddea8ae53c35b0 DE-627 ger DE-627 rakwb eng RC254-282 Qingmin Wang verfasserin aut Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundUltrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection.MethodsIn this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance.ResultsThe experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study.ConclusionThe combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals. ultrasound radiofrequency signals bispectrum analysis breast cancer deep learning weight sharing attention mechanism Neoplasms. Tumors. Oncology. Including cancer and carcinogens Xiaohong Jia verfasserin aut Ting Luo verfasserin aut Jinhua Yu verfasserin aut Shujun Xia verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 13(2023) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:13 year:2023 https://doi.org/10.3389/fonc.2023.1272427 kostenfrei https://doaj.org/article/22d3dd3f68ea4f4a9dddea8ae53c35b0 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2023.1272427/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023
allfields_unstemmed	10.3389/fonc.2023.1272427 doi (DE-627)DOAJ100021506 (DE-599)DOAJ22d3dd3f68ea4f4a9dddea8ae53c35b0 DE-627 ger DE-627 rakwb eng RC254-282 Qingmin Wang verfasserin aut Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundUltrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection.MethodsIn this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance.ResultsThe experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study.ConclusionThe combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals. ultrasound radiofrequency signals bispectrum analysis breast cancer deep learning weight sharing attention mechanism Neoplasms. Tumors. Oncology. Including cancer and carcinogens Xiaohong Jia verfasserin aut Ting Luo verfasserin aut Jinhua Yu verfasserin aut Shujun Xia verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 13(2023) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:13 year:2023 https://doi.org/10.3389/fonc.2023.1272427 kostenfrei https://doaj.org/article/22d3dd3f68ea4f4a9dddea8ae53c35b0 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2023.1272427/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023
allfieldsGer	10.3389/fonc.2023.1272427 doi (DE-627)DOAJ100021506 (DE-599)DOAJ22d3dd3f68ea4f4a9dddea8ae53c35b0 DE-627 ger DE-627 rakwb eng RC254-282 Qingmin Wang verfasserin aut Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundUltrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection.MethodsIn this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance.ResultsThe experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study.ConclusionThe combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals. ultrasound radiofrequency signals bispectrum analysis breast cancer deep learning weight sharing attention mechanism Neoplasms. Tumors. Oncology. Including cancer and carcinogens Xiaohong Jia verfasserin aut Ting Luo verfasserin aut Jinhua Yu verfasserin aut Shujun Xia verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 13(2023) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:13 year:2023 https://doi.org/10.3389/fonc.2023.1272427 kostenfrei https://doaj.org/article/22d3dd3f68ea4f4a9dddea8ae53c35b0 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2023.1272427/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023
allfieldsSound	10.3389/fonc.2023.1272427 doi (DE-627)DOAJ100021506 (DE-599)DOAJ22d3dd3f68ea4f4a9dddea8ae53c35b0 DE-627 ger DE-627 rakwb eng RC254-282 Qingmin Wang verfasserin aut Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundUltrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection.MethodsIn this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance.ResultsThe experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study.ConclusionThe combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals. ultrasound radiofrequency signals bispectrum analysis breast cancer deep learning weight sharing attention mechanism Neoplasms. Tumors. Oncology. Including cancer and carcinogens Xiaohong Jia verfasserin aut Ting Luo verfasserin aut Jinhua Yu verfasserin aut Shujun Xia verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 13(2023) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:13 year:2023 https://doi.org/10.3389/fonc.2023.1272427 kostenfrei https://doaj.org/article/22d3dd3f68ea4f4a9dddea8ae53c35b0 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2023.1272427/full kostenfrei https://doaj.org/toc/2234-943X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023
language	English
source	In Frontiers in Oncology 13(2023) volume:13 year:2023
sourceStr	In Frontiers in Oncology 13(2023) volume:13 year:2023
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institution	findex.gbv.de
topic_facet	ultrasound radiofrequency signals bispectrum analysis breast cancer deep learning weight sharing attention mechanism Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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container_title	Frontiers in Oncology
authorswithroles_txt_mv	Qingmin Wang @@aut@@ Xiaohong Jia @@aut@@ Ting Luo @@aut@@ Jinhua Yu @@aut@@ Shujun Xia @@aut@@
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As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection.MethodsIn this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance.ResultsThe experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study.ConclusionThe combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ultrasound radiofrequency signals</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bispectrum analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">breast cancer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">weight sharing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">attention mechanism</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neoplasms. 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callnumber-first	R - Medicine
author	Qingmin Wang
spellingShingle	Qingmin Wang misc RC254-282 misc ultrasound radiofrequency signals misc bispectrum analysis misc breast cancer misc deep learning misc weight sharing misc attention mechanism misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer
authorStr	Qingmin Wang
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topic_title	RC254-282 Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer ultrasound radiofrequency signals bispectrum analysis breast cancer deep learning weight sharing attention mechanism
topic	misc RC254-282 misc ultrasound radiofrequency signals misc bispectrum analysis misc breast cancer misc deep learning misc weight sharing misc attention mechanism misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc ultrasound radiofrequency signals misc bispectrum analysis misc breast cancer misc deep learning misc weight sharing misc attention mechanism misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc ultrasound radiofrequency signals misc bispectrum analysis misc breast cancer misc deep learning misc weight sharing misc attention mechanism misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer
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title_full	Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer
author_sort	Qingmin Wang
journal	Frontiers in Oncology
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author_browse	Qingmin Wang Xiaohong Jia Ting Luo Jinhua Yu Shujun Xia
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title_sort	deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer
callnumber	RC254-282
title_auth	Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer
abstract	BackgroundUltrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection.MethodsIn this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance.ResultsThe experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study.ConclusionThe combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals.
abstractGer	BackgroundUltrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection.MethodsIn this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance.ResultsThe experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study.ConclusionThe combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals.
abstract_unstemmed	BackgroundUltrasonography is an important imaging method for clinical breast cancer screening. As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection.MethodsIn this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance.ResultsThe experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study.ConclusionThe combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals.
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title_short	Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer
url	https://doi.org/10.3389/fonc.2023.1272427 https://doaj.org/article/22d3dd3f68ea4f4a9dddea8ae53c35b0 https://www.frontiersin.org/articles/10.3389/fonc.2023.1272427/full https://doaj.org/toc/2234-943X
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author2	Xiaohong Jia Ting Luo Jinhua Yu Shujun Xia
author2Str	Xiaohong Jia Ting Luo Jinhua Yu Shujun Xia
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doi_str	10.3389/fonc.2023.1272427
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up_date	2024-07-04T01:18:11.617Z
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As the original echo signals of ultrasonography, ultrasound radiofrequency (RF) signals provide abundant tissue macroscopic and microscopic information and have important development and utilization value in breast cancer detection.MethodsIn this study, we proposed a deep learning method based on bispectrum analysis feature maps to process RF signals and realize breast cancer detection. The bispectrum analysis energy feature maps with frequency subdivision were first proposed and applied to breast cancer detection in this study. Our deep learning network was based on a weight sharing network framework for the input of multiple feature maps. A feature map attention module was designed for multiple feature maps input of the network to adaptively learn both feature maps and features that were conducive to classification. We also designed a similarity constraint factor, learning the similarity and difference between feature maps by cosine distance.ResultsThe experiment results showed that the areas under the receiver operating characteristic curves of our proposed method in the validation set and two independent test sets for benign and malignant breast tumor classification were 0.913, 0.900, and 0.885, respectively. The performance of the model combining four ultrasound bispectrum analysis energy feature maps in breast cancer detection was superior to that of the model using an ultrasound grayscale image and the model using a single bispectrum analysis energy feature map in this study.ConclusionThe combination of deep learning technology and our proposed ultrasound bispectrum analysis energy feature maps effectively realized breast cancer detection and was an efficient method of feature extraction and utilization of ultrasound RF signals.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ultrasound radiofrequency signals</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">bispectrum analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">breast cancer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">deep learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">weight sharing</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">attention mechanism</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neoplasms. 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Deep learning algorithm using bispectrum analysis energy feature maps based on ultrasound radiofrequency signals to detect breast cancer

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