TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound.

Journal: International journal of computer assisted radiology and surgery
PMID: 39976857

Abstract

PURPOSE: While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspicious lesions from trans-rectal ultrasound (TRUS), they must overcome multiple simultaneous challenges. There is high heterogeneity in tissue appearance, significant class imbalance in favor of benign examples, and scarcity in the number and quality of ground truth annotations available to train models. Failure to address even a single one of these problems can result in unacceptable clinical outcomes.

Authors

  • Mohamed Harmanani
    School of Computing, Queen's University, Kingston, Canada.
  • Paul F R Wilson
    School of Computing, Queen's University, Kingston, Canada. 1pfrw@queensu.ca.
  • Minh Nguyen Nhat To
    Department of Computer Science and Engineering, Sejong University, Seoul, 05006, South Korea.
  • Mahdi Gilany
    School of Computing, Queen's University, Kingston, Canada.
  • Amoon Jamzad
    School of Computing, Queen's University, Kingston, ON, Canada.
  • Fahimeh Fooladgar
    Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.
  • Brian Wodlinger
    Exact Imaging, Markham, Canada.
  • Purang Abolmaesumi
  • Parvin Mousavi
    Medical Informatics Laboratory, School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.

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