LensePro: label noise-tolerant prototype-based network for improving cancer detection in prostate ultrasound with limited annotations.

Journal: International journal of computer assisted radiology and surgery
Published Date: Apr 10, 2024

Abstract

PURPOSE: The standard of care for prostate cancer (PCa) diagnosis is the histopathological analysis of tissue samples obtained via transrectal ultrasound (TRUS) guided biopsy. Models built with deep neural networks (DNNs) hold the potential for direct PCa detection from TRUS, which allows targeted biopsy and subsequently enhances outcomes. Yet, there are ongoing challenges with training robust models, stemming from issues such as noisy labels, out-of-distribution (OOD) data, and limited labeled data.

Authors

  • Minh Nguyen Nhat To
    Department of Computer Science and Engineering, Sejong University, Seoul, 05006, South Korea.
  • Fahimeh Fooladgar
    Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.
  • Paul Wilson
    School of Computing, Queen's University, Kingston, Canada.
  • Mohamed Harmanani
    School of Computing, Queen's University, Kingston, Canada.
  • Mahdi Gilany
    School of Computing, Queen's University, Kingston, Canada.
  • Samira Sojoudi
    Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.
  • Amoon Jamzad
    School of Computing, Queen's University, Kingston, ON, Canada.
  • Silvia Chang
    Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.
  • Peter Black
    Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.
  • Parvin Mousavi
    Medical Informatics Laboratory, School of Computing, Queen's University, 557 Goodwin Hall, Kingston, ON K7L 2N8, Canada.
  • Purang Abolmaesumi

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