MRI-based prostate cancer detection with high-level representation and hierarchical classification.

Journal: Medical physics
Published Date: Mar 1, 2017

Abstract

PURPOSE: Extracting the high-level feature representation by using deep neural networks for detection of prostate cancer, and then based on high-level feature representation constructing hierarchical classification to refine the detection results.

Authors

  • Yulian Zhu
    Computer Center, Nanjing University of Aeronautics & Astronautics, Jiangsu, China.
  • Li Wang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Mingxia Liu
    Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Chunjun Qian
    School of Science, Nanjing University of Science and Technology, Jiangsu, China.
  • Ambereen Yousuf
    Department of Radiology, Section of Urology, University of Chicago, Chicago, IL, USA.
  • Aytekin Oto
    Department of Radiology, Section of Urology, University of Chicago, Chicago, IL, USA.
  • Dinggang Shen
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.

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