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Simultaneous Segmentation and Classification of Mass Region From Mammograms Using a Mixed-Supervision Guided Deep Model

文献类型：期刊论文

作者Shen, Tianyu1,3; Gou, Chao2; Wang, Jiangong1,3; Wang, Fei-Yue1
刊名IEEE SIGNAL PROCESSING LETTERS
出版日期2020
卷号27页码:196-200
关键词Mixed-supervision deep learning segmentation and classification mammogram
ISSN号1070-9908
DOI10.1109/LSP.2019.2963151
通讯作者Gou, Chao(gouchao.cas@gmail.com)
英文摘要Automatic 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.
WOS关键词FEATURES ; NETWORK ; IMAGES
资助项目National Natural Science Foundation of China[61806198] ; National Natural Science Foundation of China[61533019]
WOS研究方向Engineering
语种英语
WOS记录号WOS:000511411900010
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/28628]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Gou, Chao
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Guangzhou 510275, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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