RSNA-MICCAI小组讨论：2. 充分发挥人工智能放射科医生和数据科学家合作的全部潜力。
RSNA-MICCAI Panel Discussion: 2. Leveraging the Full Potential of AI-Radiologists and Data Scientists Working Together.
作者信息
Linguraru Marius George, Maier-Hein Lena, Summers Ronald M, Kahn Charles E
机构信息
Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC (M.G.L.); Department of Computer Assisted Medical Interventions, German Cancer Research Centre, Heidelberg, Germany (L.M.H.); Department of Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Md (R.M.S.); and Department of Radiology, University of Pennsylvania, 3400 Spruce St, 1 Silverstein, Philadelphia, PA 19104 (C.E.K.).
出版信息
Radiol Artif Intell. 2021 Oct 27;3(6):e210248. doi: 10.1148/ryai.2021210248. eCollection 2021 Nov.
In March 2021, the Radiological Society of North America hosted a virtual panel discussion with members of the Medical Image Computing and Computer Assisted Intervention Society. Both organizations share a vision to develop radiologic and medical imaging techniques through advanced quantitative imaging biomarkers and artificial intelligence. The panel addressed how radiologists and data scientists can collaborate to advance the science of AI in radiology. Adults and Pediatrics, Segmentation, Feature Detection, Quantification, Diagnosis/Classification, Prognosis/Classification © RSNA, 2021.
2021年3月，北美放射学会与医学影像计算和计算机辅助介入学会的成员共同举办了一场虚拟小组讨论。这两个组织都有一个愿景，即通过先进的定量成像生物标志物和人工智能来开发放射学和医学成像技术。该小组讨论了放射科医生和数据科学家如何合作以推动放射学中人工智能科学的发展。成人与儿科、分割、特征检测、量化、诊断/分类、预后/分类 © RSNA，2021年。
Zotero 插件