Deep lessons learned: Radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosis

Diagn Interv Imaging. 2020 Feb;101(2):111-115. doi: 10.1016/j.diii.2019.09.002. Epub 2019 Oct 16.

¹ The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, 601 North Caroline Street, Baltimore, MD 21287, USA. Electronic address: eweisbe1@jhmi.edu.
² The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, 601 North Caroline Street, Baltimore, MD 21287, USA.
³ Department of Cognitive Science, The Johns Hopkins University, Baltimore, MD, USA.
⁴ Department of Pharmacology and Molecular Science, Johns Hopkins Hospital, Baltimore, MD, USA.
⁵ Department of Pathology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Hospital, Baltimore, MD, USA.

No abstract available

Keywords: Artificial intelligence (AI); Deep learning; Pancreatic ductal adenocarcinoma (PDA); Pancreatic neuroendocrine tumor; Radiomics.

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