Deep lessons learned: Radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosis - 26/01/20
Doi : 10.1016/j.diii.2019.09.002
E.M. Weisberg a, ⁎ 
, L.C. Chu a, S. Park a, A.L. Yuille b, K.W. Kinzler c, B. Vogelstein d, E.K. Fishman a
, L.C. Chu a, S. Park a, A.L. Yuille b, K.W. Kinzler c, B. Vogelstein d, E.K. Fishman a a The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, 601 North Caroline Street, Baltimore, MD 21287, USA
b Department of Cognitive Science, The Johns Hopkins University, Baltimore, MD, USA
c Department of Pharmacology and Molecular Science, Johns Hopkins Hospital, Baltimore, MD, USA
d Department of Pathology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Hospital, Baltimore, MD, USA
⁎Corresponding author.
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Keywords : Deep learning, Artificial intelligence (AI), Radiomics, Pancreatic ductal adenocarcinoma (PDA), Pancreatic neuroendocrine tumor
Esquema
© 2019
Société française de radiologie. Publicado por Elsevier Masson SAS. Todos los derechos reservados.
Vol 101 - N° 2
P. 111-115 - février 2020 Regresar al número
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