CASIA OpenIR
Simultaneous Segmentation and Classification of Mass Region From Mammograms Using a Mixed-Supervision Guided Deep Model
Shen, Tianyu1,2; Gou, Chao3; Wang, Jiangong1,2; Wang, Fei-Yue1
Source PublicationIEEE SIGNAL PROCESSING LETTERS
ISSN1070-9908
2020
Volume27Pages:196-200
Corresponding AuthorGou, Chao(gouchao.cas@gmail.com)
AbstractAutomatic diagnosis based on medical imaging necessitates both lesion segmentation and disease classification. Lesion segmentation requires pixel-level annotations while disease classification only requires image-level annotations. The two tasks are usually studied separately despite the latter problem relies on the former. Motivated by the close correlation between them, we propose a mixed-supervision guided method and a residual-aided classification U-Net model (ResCU-Net) for joint segmentation and benign-malignant classification. By coupling the strong supervision in the form of segmentation mask and weak supervision in the form of benign-malignant label through a simple annotation procedure, our method efficiently segments tumor regions while simultaneously predicting a discriminative map for identifying the benign-malignant types of tumors. Our network, ResCU-Net, extends U-Net by incorporating the residual module and the SegNet architecture to exploit multilevel information for achieving improved tissue identification. With experiments on a public mammogram database of INbreast, we validate the effectiveness of our method and achieve consistent improvements over state-of-the-art models.
KeywordMixed-supervision deep learning segmentation and classification mammogram
DOI10.1109/LSP.2019.2963151
WOS KeywordFEATURES ; NETWORK ; IMAGES
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61806198] ; National Natural Science Foundation of China[61533019] ; National Natural Science Foundation of China[61806198] ; National Natural Science Foundation of China[61533019]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaEngineering
WOS SubjectEngineering, Electrical & Electronic
WOS IDWOS:000511411900010
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28629
Collection中国科学院自动化研究所
Corresponding AuthorGou, Chao
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510275, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Shen, Tianyu,Gou, Chao,Wang, Jiangong,et al. Simultaneous Segmentation and Classification of Mass Region From Mammograms Using a Mixed-Supervision Guided Deep Model[J]. IEEE SIGNAL PROCESSING LETTERS,2020,27:196-200.
APA Shen, Tianyu,Gou, Chao,Wang, Jiangong,&Wang, Fei-Yue.(2020).Simultaneous Segmentation and Classification of Mass Region From Mammograms Using a Mixed-Supervision Guided Deep Model.IEEE SIGNAL PROCESSING LETTERS,27,196-200.
MLA Shen, Tianyu,et al."Simultaneous Segmentation and Classification of Mass Region From Mammograms Using a Mixed-Supervision Guided Deep Model".IEEE SIGNAL PROCESSING LETTERS 27(2020):196-200.
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