김남국 교수님 의료인공지능 세미나에 초청강의가 진행됩니다. 강연자:모두의 연구소 이일구 박사님 일시: 2019.06.11(화) 15:00 장소: 교육연구관2층 3세미나실

'3분 딥러닝 케라스맛'을 맛보기 (인공지능의료 소개) - 강연자: 김성진 박사 (LG전자 CTO 산하 인공지능연구소 상무) - 일시: 2019.06.13 (목) 19시 - 장소: 교육연구관 4층 회의실 간단한 다과 제공

을지대학교 석좌교수님이신 이홍규 교수 일시: 4월 16일 13:30~15:00 장소: 대학원 제3세미나실 강의 제목: Body size, metabolic scaling and the images(신체의 크기, 대사율 법칙과 의료 영상

정지훈 교수님 MI2RL 강의 시간: 4월 25일 (목요일)⋅10:30~11:45 장소: 서울아산병원, 아산생명과학 연구원, 융합연구관 7층 회의실

Deep learning is getting more important in modern medicine including radiology, pathology, surgery, neuroscience, etc. In case of radiology, there are several shortcomings in case of typical diagnostic radiology, due to the qualitative reading of a human observer. In addition, the rapid development of recent medical imaging equipment which produce a tremendous amount of image data makes the typical medical image reading nearly impractical. Recently, deep learning shows better accuracy for detection and classification in computer vision, which could be rapidly applied to medical imaging areas. I'll introduce methodology of data science including machine learning, and deep learning, and deep learning based applications in computer vision, computer aided diagnosis in medicine. In addition, I'll suggest some practical considerations on application of these technology to clinical workflow including efficient labeling technology, interpretability and visualization (No blackbox), uncertainty (Data level, Decision level), reproducibility of deep learning, novelty in supervised learning, one-shot or multi-shot learning due to Imbalanced data set or rare disease, deep survival, and physics induced machine learning. 1. Introduction to data science, machine learning, and deep learning 2. Deep learning in computer vision and applicaions 3. Deep learning for computer aided detection/diagnosis in radiology 4. Deep learning for computer aided detection/diagnosis in pathology 5. Practical consideration for deep learning application in medicine- efficient labeling technology - Interpretability and visualization (No blackbox) - Uncertainty (Data level, Decision level) - Reproducibility of deep learning - Novelty in supervised learning - One-shot or multi-shot learning due to Imbalanced data set or rare disease - Deep survival - Physics induced machine learning