Machine learning techniques for mitoses classification.

Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Published Date: Jan 1, 2021

Abstract

BACKGROUND: Pathologists analyze biopsy material at both the cellular and structural level to determine diagnosis and cancer stage. Mitotic figures are surrogate biomarkers of cellular proliferation that can provide prognostic information; thus, their precise detection is an important factor for clinical care. Convolutional Neural Networks (CNNs) have shown remarkable performance on several recognition tasks. Utilizing CNNs for mitosis classification may aid pathologists to improve the detection accuracy.

Authors

  • Shima Nofallah
    University of Washington, Seattle WA 98195, USA. Electronic address: shima@cs.washington.edu.
  • Sachin Mehta
    Department of Electrical and Computer Engineering, University of Washington, Seattle.
  • Ezgi Mercan
    Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle.
  • Stevan Knezevich
    Pathology Associates, Clovis, CA 983611, USA. Electronic address: shapiro@cs.washington.edu.
  • Caitlin J May
    University of Washington, Seattle WA 98195, USA. Electronic address: caitmay@u.washington.edu.
  • Donald Weaver
    University of Vermont, Burlington VT 05405, USA. Electronic address: donald.weaver@uvmhealth.org.
  • Daniela Witten
    University of Washington, Seattle WA 98195, USA. Electronic address: dwitten@uw.edu.
  • Joann G Elmore
    Department of Medicine, University of Washington School of Medicine, Seattle.
  • Linda Shapiro
    University of Washington, Seattle WA 98195, USA. Electronic address: shapiro@cs.washington.edu.

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