逐步转移学习专家级儿童脑瘤MRI细分在有限的数据场景

Aidan Boyd¹, Zezhong Ye¹, Sanjay P Prabhu¹+15

• ¹From the Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, Mass (A.B., Z.Y., Y.Z., A.Z., H.H., R.C., H.J.W.L.A., B.H.K.); Department of Radiation Oncology (A.B., Z.Y., M.C.T., Y.Z., A.Z., H.H., R.C., K.X.L., D.A.H.K., H.J.W.L.A., B.H.K.) and Department of Radiology (H.J.W.L.A.), Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis St, Boston, MA 02115; Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass (S.P.P., S.V., T.Y.P.); Department of Biostatistics and Computational Biology, Harvard T.H. Chan School of Public Health, Boston, Mass (P.J.C.); Center for Data-Driven Discovery in Biomedicine (D3b) (A.N., A.C.R.) and Department of Neurosurgery (A.C.R.), Children's Hospital of Philadelphia, Philadelphia, Pa; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (A.N.); Departments of Neurology, Pediatrics, and Neurologic Surgery, University of California, San Francisco, San Francisco, Calif (S.M.); and Department of Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands (H.J.W.L.A.).