Cited 112 times in
Guided Soft Attention Network for Classification of Breast Cancer Histopathology Images
DC Field | Value | Language |
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dc.date.accessioned | 2023-02-10T01:58:34Z | - |
dc.date.available | 2023-02-10T01:58:34Z | - |
dc.date.issued | 2020-05 | - |
dc.identifier.issn | 0278-0062 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/192422 | - |
dc.description.abstract | An attention guided convolutional neural network (CNN) for the classification of breast cancer histopathology images is proposed. Neural networks are generally applied as black box models and often the network's decisions are difficult to interpret. Making the decision process transparent, and hence reliable is important for a computer-assisted diagnosis (CAD) system. Moreover, it is crucial that the network's decision be based on histopathological features that are in agreement with a human expert. To this end, we propose to use additional region-level supervision for the classification of breast cancer histopathology images using CNN, where the regions of interest (RoI) are localized and used to guide the attention of the classification network simultaneously. The proposed supervised attention mechanism specifically activates neurons in diagnostically relevant regions while suppressing activations in irrelevant and noisy areas. The class activation maps generated by the proposed method correlate well with the expectations of an expert pathologist. Moreover, the proposed method surpasses the state-of-the-art on the BACH microscopy test dataset (part A) with a significant margin. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON MEDICAL IMAGING | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Breast Neoplasms* / diagnostic imaging | - |
dc.subject.MESH | Diagnosis, Computer-Assisted | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Microscopy | - |
dc.subject.MESH | Neural Networks, Computer | - |
dc.title | Guided Soft Attention Network for Classification of Breast Cancer Histopathology Images | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Pathology (병리학교실) | - |
dc.contributor.googleauthor | Heechan Yang | - |
dc.contributor.googleauthor | Ji-Ye Kim | - |
dc.contributor.googleauthor | Hyongsuk Kim | - |
dc.contributor.googleauthor | Shyam P Adhikari | - |
dc.identifier.doi | 10.1109/TMI.2019.2948026 | - |
dc.relation.journalcode | J01028 | - |
dc.identifier.pmid | 31634125 | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/8873545/ | - |
dc.citation.volume | 39 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 1306 | - |
dc.citation.endPage | 1315 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol.39(5) : 1306-1315, 2020-05 | - |
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