A mix-pooling CNN architecture with FCRF for brain tumor segmentation
MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture w...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Chang, Jie [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
7 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Propolis as lipid bioactive nano-carrier for topical nasal drug delivery - Rassu, Giovanna ELSEVIER, 2015, Orlando, Fla |
|---|---|
| Übergeordnetes Werk: |
volume:58 ; year:2019 ; pages:316-322 ; extent:7 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jvcir.2018.11.047 |
|---|
| Katalog-ID: |
ELV045447152 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV045447152 | ||
| 003 | DE-627 | ||
| 005 | 20230626011445.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 190205s2019 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.jvcir.2018.11.047 |2 doi | |
| 028 | 5 | 2 | |a GBV00000000000488.pica |
| 035 | |a (DE-627)ELV045447152 | ||
| 035 | |a (ELSEVIER)S1047-3203(18)30326-2 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 540 |q VZ |
| 082 | 0 | 4 | |a 540 |q VZ |
| 100 | 1 | |a Chang, Jie |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A mix-pooling CNN architecture with FCRF for brain tumor segmentation |
| 264 | 1 | |c 2019transfer abstract | |
| 300 | |a 7 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. | ||
| 520 | |a MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. | ||
| 650 | 7 | |a Convolutional Neural Network |2 Elsevier | |
| 650 | 7 | |a MR image segmentation |2 Elsevier | |
| 650 | 7 | |a Fully CRF |2 Elsevier | |
| 700 | 1 | |a Zhang, Luming |4 oth | |
| 700 | 1 | |a Gu, Naijie |4 oth | |
| 700 | 1 | |a Zhang, Xiaoci |4 oth | |
| 700 | 1 | |a Ye, Minquan |4 oth | |
| 700 | 1 | |a Yin, Rongzhang |4 oth | |
| 700 | 1 | |a Meng, Qianqian |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Academic Press |a Rassu, Giovanna ELSEVIER |t Propolis as lipid bioactive nano-carrier for topical nasal drug delivery |d 2015 |g Orlando, Fla |w (DE-627)ELV023814993 |
| 773 | 1 | 8 | |g volume:58 |g year:2019 |g pages:316-322 |g extent:7 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.jvcir.2018.11.047 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a GBV_ILN_11 | ||
| 912 | |a GBV_ILN_21 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_50 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_72 | ||
| 912 | |a GBV_ILN_136 | ||
| 912 | |a GBV_ILN_162 | ||
| 912 | |a GBV_ILN_165 | ||
| 912 | |a GBV_ILN_176 | ||
| 912 | |a GBV_ILN_181 | ||
| 912 | |a GBV_ILN_203 | ||
| 912 | |a GBV_ILN_227 | ||
| 912 | |a GBV_ILN_352 | ||
| 912 | |a GBV_ILN_676 | ||
| 912 | |a GBV_ILN_791 | ||
| 912 | |a GBV_ILN_1018 | ||
| 951 | |a AR | ||
| 952 | |d 58 |j 2019 |h 316-322 |g 7 | ||
| author_variant |
j c jc |
|---|---|
| matchkey_str |
changjiezhangluminggunaijiezhangxiaociye:2019----:mxolncnrhtcueihcfoban |
| hierarchy_sort_str |
2019transfer abstract |
| publishDate |
2019 |
| allfields |
10.1016/j.jvcir.2018.11.047 doi GBV00000000000488.pica (DE-627)ELV045447152 (ELSEVIER)S1047-3203(18)30326-2 DE-627 ger DE-627 rakwb eng 540 VZ 540 VZ Chang, Jie verfasserin aut A mix-pooling CNN architecture with FCRF for brain tumor segmentation 2019transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. Convolutional Neural Network Elsevier MR image segmentation Elsevier Fully CRF Elsevier Zhang, Luming oth Gu, Naijie oth Zhang, Xiaoci oth Ye, Minquan oth Yin, Rongzhang oth Meng, Qianqian oth Enthalten in Academic Press Rassu, Giovanna ELSEVIER Propolis as lipid bioactive nano-carrier for topical nasal drug delivery 2015 Orlando, Fla (DE-627)ELV023814993 volume:58 year:2019 pages:316-322 extent:7 https://doi.org/10.1016/j.jvcir.2018.11.047 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_50 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_136 GBV_ILN_162 GBV_ILN_165 GBV_ILN_176 GBV_ILN_181 GBV_ILN_203 GBV_ILN_227 GBV_ILN_352 GBV_ILN_676 GBV_ILN_791 GBV_ILN_1018 AR 58 2019 316-322 7 |
| spelling |
10.1016/j.jvcir.2018.11.047 doi GBV00000000000488.pica (DE-627)ELV045447152 (ELSEVIER)S1047-3203(18)30326-2 DE-627 ger DE-627 rakwb eng 540 VZ 540 VZ Chang, Jie verfasserin aut A mix-pooling CNN architecture with FCRF for brain tumor segmentation 2019transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. Convolutional Neural Network Elsevier MR image segmentation Elsevier Fully CRF Elsevier Zhang, Luming oth Gu, Naijie oth Zhang, Xiaoci oth Ye, Minquan oth Yin, Rongzhang oth Meng, Qianqian oth Enthalten in Academic Press Rassu, Giovanna ELSEVIER Propolis as lipid bioactive nano-carrier for topical nasal drug delivery 2015 Orlando, Fla (DE-627)ELV023814993 volume:58 year:2019 pages:316-322 extent:7 https://doi.org/10.1016/j.jvcir.2018.11.047 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_50 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_136 GBV_ILN_162 GBV_ILN_165 GBV_ILN_176 GBV_ILN_181 GBV_ILN_203 GBV_ILN_227 GBV_ILN_352 GBV_ILN_676 GBV_ILN_791 GBV_ILN_1018 AR 58 2019 316-322 7 |
| allfields_unstemmed |
10.1016/j.jvcir.2018.11.047 doi GBV00000000000488.pica (DE-627)ELV045447152 (ELSEVIER)S1047-3203(18)30326-2 DE-627 ger DE-627 rakwb eng 540 VZ 540 VZ Chang, Jie verfasserin aut A mix-pooling CNN architecture with FCRF for brain tumor segmentation 2019transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. Convolutional Neural Network Elsevier MR image segmentation Elsevier Fully CRF Elsevier Zhang, Luming oth Gu, Naijie oth Zhang, Xiaoci oth Ye, Minquan oth Yin, Rongzhang oth Meng, Qianqian oth Enthalten in Academic Press Rassu, Giovanna ELSEVIER Propolis as lipid bioactive nano-carrier for topical nasal drug delivery 2015 Orlando, Fla (DE-627)ELV023814993 volume:58 year:2019 pages:316-322 extent:7 https://doi.org/10.1016/j.jvcir.2018.11.047 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_50 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_136 GBV_ILN_162 GBV_ILN_165 GBV_ILN_176 GBV_ILN_181 GBV_ILN_203 GBV_ILN_227 GBV_ILN_352 GBV_ILN_676 GBV_ILN_791 GBV_ILN_1018 AR 58 2019 316-322 7 |
| allfieldsGer |
10.1016/j.jvcir.2018.11.047 doi GBV00000000000488.pica (DE-627)ELV045447152 (ELSEVIER)S1047-3203(18)30326-2 DE-627 ger DE-627 rakwb eng 540 VZ 540 VZ Chang, Jie verfasserin aut A mix-pooling CNN architecture with FCRF for brain tumor segmentation 2019transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. Convolutional Neural Network Elsevier MR image segmentation Elsevier Fully CRF Elsevier Zhang, Luming oth Gu, Naijie oth Zhang, Xiaoci oth Ye, Minquan oth Yin, Rongzhang oth Meng, Qianqian oth Enthalten in Academic Press Rassu, Giovanna ELSEVIER Propolis as lipid bioactive nano-carrier for topical nasal drug delivery 2015 Orlando, Fla (DE-627)ELV023814993 volume:58 year:2019 pages:316-322 extent:7 https://doi.org/10.1016/j.jvcir.2018.11.047 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_50 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_136 GBV_ILN_162 GBV_ILN_165 GBV_ILN_176 GBV_ILN_181 GBV_ILN_203 GBV_ILN_227 GBV_ILN_352 GBV_ILN_676 GBV_ILN_791 GBV_ILN_1018 AR 58 2019 316-322 7 |
| allfieldsSound |
10.1016/j.jvcir.2018.11.047 doi GBV00000000000488.pica (DE-627)ELV045447152 (ELSEVIER)S1047-3203(18)30326-2 DE-627 ger DE-627 rakwb eng 540 VZ 540 VZ Chang, Jie verfasserin aut A mix-pooling CNN architecture with FCRF for brain tumor segmentation 2019transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. Convolutional Neural Network Elsevier MR image segmentation Elsevier Fully CRF Elsevier Zhang, Luming oth Gu, Naijie oth Zhang, Xiaoci oth Ye, Minquan oth Yin, Rongzhang oth Meng, Qianqian oth Enthalten in Academic Press Rassu, Giovanna ELSEVIER Propolis as lipid bioactive nano-carrier for topical nasal drug delivery 2015 Orlando, Fla (DE-627)ELV023814993 volume:58 year:2019 pages:316-322 extent:7 https://doi.org/10.1016/j.jvcir.2018.11.047 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_50 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_136 GBV_ILN_162 GBV_ILN_165 GBV_ILN_176 GBV_ILN_181 GBV_ILN_203 GBV_ILN_227 GBV_ILN_352 GBV_ILN_676 GBV_ILN_791 GBV_ILN_1018 AR 58 2019 316-322 7 |
| language |
English |
| source |
Enthalten in Propolis as lipid bioactive nano-carrier for topical nasal drug delivery Orlando, Fla volume:58 year:2019 pages:316-322 extent:7 |
| sourceStr |
Enthalten in Propolis as lipid bioactive nano-carrier for topical nasal drug delivery Orlando, Fla volume:58 year:2019 pages:316-322 extent:7 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Convolutional Neural Network MR image segmentation Fully CRF |
| dewey-raw |
540 |
| isfreeaccess_bool |
false |
| container_title |
Propolis as lipid bioactive nano-carrier for topical nasal drug delivery |
| authorswithroles_txt_mv |
Chang, Jie @@aut@@ Zhang, Luming @@oth@@ Gu, Naijie @@oth@@ Zhang, Xiaoci @@oth@@ Ye, Minquan @@oth@@ Yin, Rongzhang @@oth@@ Meng, Qianqian @@oth@@ |
| publishDateDaySort_date |
2019-01-01T00:00:00Z |
| hierarchy_top_id |
ELV023814993 |
| dewey-sort |
3540 |
| id |
ELV045447152 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV045447152</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626011445.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">190205s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jvcir.2018.11.047</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000488.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV045447152</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1047-3203(18)30326-2</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="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chang, Jie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A mix-pooling CNN architecture with FCRF for brain tumor segmentation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">7</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Convolutional Neural Network</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MR image segmentation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fully CRF</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Luming</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gu, Naijie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Xiaoci</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ye, Minquan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yin, Rongzhang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Meng, Qianqian</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academic Press</subfield><subfield code="a">Rassu, Giovanna ELSEVIER</subfield><subfield code="t">Propolis as lipid bioactive nano-carrier for topical nasal drug delivery</subfield><subfield code="d">2015</subfield><subfield code="g">Orlando, Fla</subfield><subfield code="w">(DE-627)ELV023814993</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:58</subfield><subfield code="g">year:2019</subfield><subfield code="g">pages:316-322</subfield><subfield code="g">extent:7</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jvcir.2018.11.047</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_50</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_72</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_136</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_162</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_165</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_176</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_181</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_203</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_227</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_352</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_676</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_791</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_1018</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">58</subfield><subfield code="j">2019</subfield><subfield code="h">316-322</subfield><subfield code="g">7</subfield></datafield></record></collection>
|
| author |
Chang, Jie |
| spellingShingle |
Chang, Jie ddc 540 Elsevier Convolutional Neural Network Elsevier MR image segmentation Elsevier Fully CRF A mix-pooling CNN architecture with FCRF for brain tumor segmentation |
| authorStr |
Chang, Jie |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV023814993 |
| format |
electronic Article |
| dewey-ones |
540 - Chemistry & allied sciences |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
540 VZ A mix-pooling CNN architecture with FCRF for brain tumor segmentation Convolutional Neural Network Elsevier MR image segmentation Elsevier Fully CRF Elsevier |
| topic |
ddc 540 Elsevier Convolutional Neural Network Elsevier MR image segmentation Elsevier Fully CRF |
| topic_unstemmed |
ddc 540 Elsevier Convolutional Neural Network Elsevier MR image segmentation Elsevier Fully CRF |
| topic_browse |
ddc 540 Elsevier Convolutional Neural Network Elsevier MR image segmentation Elsevier Fully CRF |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
l z lz n g ng x z xz m y my r y ry q m qm |
| hierarchy_parent_title |
Propolis as lipid bioactive nano-carrier for topical nasal drug delivery |
| hierarchy_parent_id |
ELV023814993 |
| dewey-tens |
540 - Chemistry |
| hierarchy_top_title |
Propolis as lipid bioactive nano-carrier for topical nasal drug delivery |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV023814993 |
| title |
A mix-pooling CNN architecture with FCRF for brain tumor segmentation |
| ctrlnum |
(DE-627)ELV045447152 (ELSEVIER)S1047-3203(18)30326-2 |
| title_full |
A mix-pooling CNN architecture with FCRF for brain tumor segmentation |
| author_sort |
Chang, Jie |
| journal |
Propolis as lipid bioactive nano-carrier for topical nasal drug delivery |
| journalStr |
Propolis as lipid bioactive nano-carrier for topical nasal drug delivery |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
500 - Science |
| recordtype |
marc |
| publishDateSort |
2019 |
| contenttype_str_mv |
zzz |
| container_start_page |
316 |
| author_browse |
Chang, Jie |
| container_volume |
58 |
| physical |
7 |
| class |
540 VZ |
| format_se |
Elektronische Aufsätze |
| author-letter |
Chang, Jie |
| doi_str_mv |
10.1016/j.jvcir.2018.11.047 |
| dewey-full |
540 |
| title_sort |
a mix-pooling cnn architecture with fcrf for brain tumor segmentation |
| title_auth |
A mix-pooling CNN architecture with FCRF for brain tumor segmentation |
| abstract |
MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. |
| abstractGer |
MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. |
| abstract_unstemmed |
MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_11 GBV_ILN_21 GBV_ILN_22 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_50 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_136 GBV_ILN_162 GBV_ILN_165 GBV_ILN_176 GBV_ILN_181 GBV_ILN_203 GBV_ILN_227 GBV_ILN_352 GBV_ILN_676 GBV_ILN_791 GBV_ILN_1018 |
| title_short |
A mix-pooling CNN architecture with FCRF for brain tumor segmentation |
| url |
https://doi.org/10.1016/j.jvcir.2018.11.047 |
| remote_bool |
true |
| author2 |
Zhang, Luming Gu, Naijie Zhang, Xiaoci Ye, Minquan Yin, Rongzhang Meng, Qianqian |
| author2Str |
Zhang, Luming Gu, Naijie Zhang, Xiaoci Ye, Minquan Yin, Rongzhang Meng, Qianqian |
| ppnlink |
ELV023814993 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth oth oth oth |
| doi_str |
10.1016/j.jvcir.2018.11.047 |
| up_date |
2024-07-06T17:33:52.207Z |
| _version_ |
1803851904799735808 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV045447152</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626011445.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">190205s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jvcir.2018.11.047</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000488.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV045447152</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S1047-3203(18)30326-2</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="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chang, Jie</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A mix-pooling CNN architecture with FCRF for brain tumor segmentation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">7</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">MR technique is prevalent for doctor to diagnose and assess glioblastomas which are the most lethal form of brain tumors. Although Convolutional Neural Networks (CNN) has been applied in automatic brain tumor segmentation and is proved useful and efficient, traditional one-pathway CNN architecture with convolutional layers and max pooling layers has limited receptive fields representing the local context information. Such mindset in traditional CNN may dismiss useful global context information. In this paper, we design a two-pathway model with average and max pooling layers in different paths. Besides, 1 × 1 kernels are followed input layers to add the non-linearity dimensions of input data. Finally, we combine the CNN architecture with fully connected CRF(FCRF) as a mixture model to introduce the global context information to optimize prediction results. Our experiments proved that the mixture model improved segmentation and labeling accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Convolutional Neural Network</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MR image segmentation</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fully CRF</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Luming</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gu, Naijie</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhang, Xiaoci</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ye, Minquan</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yin, Rongzhang</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Meng, Qianqian</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Academic Press</subfield><subfield code="a">Rassu, Giovanna ELSEVIER</subfield><subfield code="t">Propolis as lipid bioactive nano-carrier for topical nasal drug delivery</subfield><subfield code="d">2015</subfield><subfield code="g">Orlando, Fla</subfield><subfield code="w">(DE-627)ELV023814993</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:58</subfield><subfield code="g">year:2019</subfield><subfield code="g">pages:316-322</subfield><subfield code="g">extent:7</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.jvcir.2018.11.047</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_21</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_50</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_72</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_136</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_162</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_165</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_176</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_181</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_203</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_227</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_352</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_676</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_791</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_1018</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">58</subfield><subfield code="j">2019</subfield><subfield code="h">316-322</subfield><subfield code="g">7</subfield></datafield></record></collection>
|
| score |
7.4008417 |