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RSNA-MICCAI Panel Discussion: Machine Learning for Radiology from Challenges to Clinical Applications

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RADIOLOGICAL SOC NORTH AMERICA (RSNA)
DOI: 10.1148/ryai.2021210118

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Back-Propagation; Artificial Neural Network Algorithms; Machine Learning Algorithms

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The MICCAI and RSNA societies held a virtual panel discussion to facilitate collaboration between radiologists and machine learning scientists for the advancement of medical imaging techniques. The discussion covered innovation and clinical success of imaging technology, emphasizing the importance of collaboration.
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. (C) RSNA, 2021

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