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

Abstract

Authors

• E M Weisberg
  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.
• L C Chu
  The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, School of Medicine, 601N. Caroline Street, Baltimore, MD 21287, USA.
• S Park
• A L Yuille
  Department of Computer Science, Johns Hopkins University, School of Arts and Sciences, Baltimore, MD 21218, USA.
• K W Kinzler
  Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA; Johns Hopkins University, School of Medicine, Ludwig Center for Cancer Genetics and Therapeutics, Baltimore, MD 21205, USA.
• B Vogelstein
  Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA; Johns Hopkins University, School of Medicine, Ludwig Center for Cancer Genetics and Therapeutics, Baltimore, MD 21205, USA.
• E K Fishman
  The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, School of Medicine, 601N. Caroline Street, Baltimore, MD 21287, USA.

Keywords

External Resources