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Curation of the CANDID-PTX Dataset with Free-Text Reports

Sijing Feng1, Damian Azzollini1, Ji Soo Kim1

  • 1Department of Radiology, Dunedin Hospital, 201 Great King St, Dunedin Central, Dunedin, Otago 9016, New Zealand (S.F., A.F., C.S., B.W.); Eastern Health, Melbourne, Victoria, Australia (D.A.); Auckland District Health Board, Auckland, New Zealand (J.S.K., J.Y., E.K., M.H.); Waitemata District Health Board, Auckland, New Zealand (C.K.J.); Waikato District Health Board, Hamilton, New Zealand (S.P.G.); The University of Auckland Faculty of Medical and Health Sciences, Auckland, Auckland, New Zealand (A.L.); University of Otago Medical School, Dunedin, Otago, New Zealand (A.P.); IBM Almaden Research Center, San Jose, Calif (J.W.); School of Computer Science, University of Auckland, Auckland, New Zealand (M.U.); Department of Radiology, Auckland City Hospital, Auckland, New Zealand (G.P.T.); and Department of Radiology, Middlemore Hospital, Auckland, New Zealand (S.B.).

Radiology. Artificial Intelligence
|December 6, 2021
PubMed

Keywords:
CathetersClassificationConventional RadiographyDiagnosisMachine LearningRibsSegmentationSupervised LearningThoraxTrauma

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