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RSNA-MICCAI Panel Discussion: Machine Learning for Radiology from Challenges to Clinical Applications
John Mongan1, Jayashree Kalpathy-Cramer1, Adam Flanders1
1Department of Radiology and Biomedical Imaging and Center for Intelligent Imaging, University of California San Francisco, 505 Parnassus Ave, Room M-391, San Francisco, CA 94143 (J.M.); Department of Radiology and MGH and BWH Center for Clinical Data Science, Massachusetts General Hospital, Boston, Mass (J.K.C.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.F.); Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC (M.G.L.); and Departments of Pediatrics and Radiology, George Washington University School of Medicine, Washington, DC (M.G.L.).
Abstract:
On October 5, 2020, the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) 2020 conference hosted a virtual panel discussion with members of the Machine Learning Steering Subcommittee of the Radiological Society of North America. The MICCAI Society brings together scientists, engineers, physicians, educators, and students from around the world. Both societies share a vision to develop radiologic and medical imaging techniques through advanced quantitative imaging biomarkers and artificial intelligence. The panel elaborated on how collaborations between radiologists and machine learning scientists facilitate the creation and clinical success of imaging technology for radiology. This report presents structured highlights of the moderated dialogue at the panel. Keywords: Back-Propagation, Artificial Neural Network Algorithms, Machine Learning Algorithms © RSNA, 2021.
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