使:

Sarah E Hickman1, Nicholas R Payne1, Richard T Black1

  • 1From the Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Level 5, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK (S.E.H., N.R.P., Y.H., A.N.P., M.N., F.J.G.); University of Cambridge School of Clinical Medicine, Cambridge, UK (M.I.A, A.S.); Department of Radiology, Barts Health NHS Trust, The Royal London Hospital, London, UK (S.E.H.); Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK (R.T.B., A.N.P., F.J.G.); EPSRC Cambridge Mathematics of Information in Healthcare Hub, University of Cambridge, Cambridge, UK (Y.H.); Peel & Schriek Consulting, London, UK (S.H.); Department of Radiology, Norfolk and Norwich University Hospital, Norwich, UK (B.K., A.J.); and University of East Anglia, Norwich Research Park, Norwich, UK (B.K.).

Radiology
|November 21, 2023
PubMed
概括

深度学习 (DL) 算法可以有效地对乳房影像进行分类,识别正常结果以减少放射科医生的工作量并标记潜在的癌症. 与传统的双重阅读相比,这种适应性工作流显示出更高的灵敏度和非劣质的特异性.

相关概念视频