We assessed the feasibility of a data-driven imaging biomarker based on weakly supervised learning (DIB; an imaging biomarker derived from large-scale medical image data with deep learning technology) in mammography (DIB-MG). A total of 29,107 digital mammograms from five institutions (4,339 cancer cases and 24,768 normal cases) were included. After matching patients’ age, breast density, and equipment, 1,238 and 1,238 cases were chosen as validation and test sets, respectively, and the remainder were used for training. The core algorithm of DIB-MG is a deep convolutional neural network; a deep learning algorithm specialized for images. Each sample (case) is an exam composed of 4-view images (RCC, RMLO, LCC, and LMLO). For each case in a training set, the cancer probability inferred from DIB-MG is compared with the per-case ground-truth label. Then the model parameters in DIB-MG are updated based on the error between the prediction and the ground-truth. At the operating point (threshold) of 0.5, sensitivity was 75.6% and 76.1% when specificity was 90.2% and 88.5%, and AUC was 0.903 and 0.906 for the validation and test sets, respectively. This research showed the potential of DIB-MG as a screening tool for breast cancer.
Eun-Kyung Kim1, Hyo-Eun Kim2, Kyunghwa Han1, Bong Joo Kang3, Yu-Mee Sohn4, Ok Hee Woo5 and Chan Wha Lee6
1Severance Hospital, Yonsei University, 2Lunit Inc., 3Seoul St. Mary’s Hospital, Catholic University, 4Kyung Hee University Hospital, 5Korea University Guro Hospital, 6National Cancer Center Hospital
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Application of artificial intelligence–based computer-assisted diagnosis on synthetic mammograms from breast tomosynthesis: comparison with digital mammograms
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유방촬영의 위양성 판정에 관한 전통적 진단보조프로그램과 인공지능 기반 진단보조프로그램의 비교
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Retrospective Review of Missed Cancer Detection and Its Mammography Findings with Artificial-Intelligence-Based, Computer-Aided Diagnosis
Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics
Mammographic Density Assessment by Artificial Intelligence-Based Computer-Assisted Diagnosis: A Comparison with Automated Volumetric Assessment
Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x
Need of pretreatment support of breast cancer patient caregivers compared to patients.