학회지검색
대한유방검진의학회 학회지
Review Article
1Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 2Korea Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Korea Applications of Artificial Intelligence in Automated Breast Ultrasound Kangrok Oh, PhD1, Si Eun Lee, MD2, Eun-Kyung Kim, MD2
1Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 2Korea Department of Radiology, Yongin Severance Hospital, Yonsei University College of Medicine, Korea
Automated breast ultrasound (ABUS) is becoming a powerful complementary imaging modality of mammography. Due to its enormous data size, the exploitation of ABUS computer-aided detection and diagnosis (CAD) systems is crucial in the time-consuming screening process. Conventional ABUS-CAD systems containing extensive image processing, hand-crafted feature extraction, and classification modules suffer from low generalization capability. Recently deep learning (DL) has been applied successfully in advanced ABUS-CAD systems given sufficient data and supplementary techniques such as data augmentation and transfer learning. In this study, we investigate underlying artificial intelligence techniques of ABUS-CAD systems with their clinical applications in the literature.
Key Words: Breast; Automated Breast Ultrasound; Artificial Intelligence; Computer-Aided Detection; Computer-Aided Diagnosiss