Deep learning based classification of solid lipid-poor contrast enhancing renal masses using contrast enhanced CT.

Journal: The British journal of radiology
PMID:

Abstract

OBJECTIVE: Establish a workflow that utilizes convolutional neural nets (CNN) to classify solid, lipid-poor, contrast enhancing renal masses using multiphase contrast enhanced CT (CECT) images and to assess the performance of the resulting network.

Authors

  • Assad Oberai
    Department of Aerospace and Mechanical Engineering, Univ. of Southern California, Los Angeles, CA, USA.
  • Bino Varghese
    Department of Radiology, University of Southern California, Los Angeles, CA, USA. bino.varghese@med.usc.edu.
  • Steven Cen
    Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA, 90033, USA.
  • Tomas Angelini
    Department of Computer Science, Univ. of Southern California, Los Angeles, CA, USA.
  • Darryl Hwang
    Department of Radiology, University of Southern California, Los Angeles, CA, USA.
  • Inderbir Gill
    USC Institute of Urology, Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Manju Aron
    Department of Pathology, Keck School of Medicine, University of Southern California, 2011 Zonal Avenue, Los Angeles, CA, 90033, USA.
  • Christopher Lau
    Department of Radiology, Univ. of Southern California, Los Angeles, CA, USA.
  • Vinay Duddalwar
    Department of Radiology, Keck School of Medicine, University of Southern California, 1500 San Pablo Street, 2nd Floor, Los Angeles, CA, 90033, USA.