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小组必不可少:胸部放射学分类器概括患者,设置和病理学小组中的差距

Monish Ahluwalia1, Mohamed Abdalla1, James Sanayei1

  • 1From the Kingston Health Sciences Centre, Queen's University, Kingston, Ontario, Canada (M. Ahluwalia); Faculty of Medicine (M. Ahluwalia, J.S.), Institute of Health Policy, Management and Evaluation (M. Ahluwalia), Department of Computer Science (M. Abdalla, L.S.K.), and Department of Medical Imaging (B.F.), University of Toronto, Toronto, Ontario, Canada; Vector Institute for Artificial Intelligence, Toronto, Canada (M. Abdalla, B.F.); Institute for Better Health (M. Abdalla, A.A., B.F.) and Department of Diagnostic Imaging (A.A., B.F.), Trillium Health Partners, 100 Queensway West, Clinical Administrative Building, 6th Floor, Mississauga, ON, Canada L5B 1B8; Department of Medicine, Royal University Hospital, Saskatoon, Saskatchewan, Canada (J.S.); Department of Electrical Engineering and Computer Science, York University, Toronto, Ontario, Canada (L.S.K.); and Techie Maestro, Waterloo, Ontario, Canada (M.H.).

Radiology. Artificial intelligence
|October 5, 2023
PubMed
概括

在一个大数据集上测试了四个深度学习胸部X-ray分类器. 患者,环境和病理学子组之间的表现有显著差异,突出显示了在AI实施中需要进行子组分析的必要性.

关键词:
传统的X光学X光学卷积神经网络 (CNN) 是一种神经网络.伦理学 伦理学 伦理学机器学习算法 机器学习算法监督学习 监督学习胸部 胸部 胸部 胸部

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