A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.

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第一作者： Mugahed A,Al-Antari

第一单位： Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, 17104, Republic of Korea. Electronic address: en.mualshz@khu.ac.kr.

作者： Mugahed A,Al-Antari ^[1] ; Mohammed A,Al-Masni ^[2] ; Mun-Taek,Choi ^[3] ; Seung-Moo,Han ^[4] ; Tae-Seong,Kim ^[5]

作者单位： Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, 17104, Republic of Korea. Electronic address: en.mualshz@khu.ac.kr. ^[1] Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, 17104, Republic of Korea. Electronic address: m.almasani@khu.ac.kr. ^[2] School of Mechanical Engineering, Sungkyunkwan University, Republic of Korea. Electronic address: mtchoi@skku.edu. ^[3] Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, 17104, Republic of Korea. Electronic address: smhan@khu.ac.kr. ^[4] Department of Biomedical Engineering, College of Electronics and Information, Kyung Hee University, Yongin, 17104, Republic of Korea. Electronic address: tskim@khu.ac.kr. ^[5]

关键词 Computer-aided diagnosis (CAD)Deep learning Full resolution convolutional network (FrCN)Mass detection Mass segmentation You-only-look-once (YOLO)

主题词乳腺肿瘤(Breast Neoplasms);诊断, 计算机辅助(Diagnosis, Computer-Assisted);女(雌)性(Female);人类(Humans);乳房X线摄影术(Mammography);X线影像增强(Radiographic Image Enhancement)

DOI 10.1016/j.ijmedinf.2018.06.003

PMID 30032964

发布时间 2019-12-10

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