Deep Learning for Spine MRI: Reducing Time Not Quality.

Abstract

Authors

• James Thomas Patrick Decourcy Hallinan
  From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (J.T.P.D.H., A.M., Y.L.T., S.L., Y.S.C., S.E.E., S.T.Q.); NUS Graduate School, Integrative Sciences and Engineering Programme, National University of Singapore, Singapore (L.Z.); Department of Computer Science, School of Computing, National University of Singapore, Singapore (K.Y., B.C.O.); Department of Radiology, Dammam Medical Complex, Dammam, Saudi Arabia (D.A.R.A.); Biostatistics Unit, Yong Loo Lin School of Medicine, Singapore (Q.V.Y., Y.H.C.); University Spine Centre, Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.).

Keywords

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