A deep active learning framework for mitotic figure detection with minimal manual annotation and labelling.

Journal: Histopathology
Published Date: Jul 3, 2025

Abstract

AIMS: Accurately and efficiently identifying mitotic figures (MFs) is crucial for diagnosing and grading various cancers, including glioblastoma (GBM), a highly aggressive brain tumour requiring precise and timely intervention. Traditional manual counting of MFs in whole slide images (WSIs) is labour-intensive and prone to interobserver variability. Our study introduces a deep active learning framework that addresses these challenges with minimal human intervention.

Authors

  • Eric Liu
    Bayes Impact, Technology 501(c)(3) Non-profit, San Francisco, California, United States of America.
  • August Lin
    Department of Pathology and Lab Medicine, Western University, London, Ontario, Canada.
  • Pramath Kakodkar
    Department of Pathology and Lab Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
  • Yayuan Zhao
    Department of Pathology and Lab Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.
  • Boyu Wang
  • Charles Ling
    Department of Computer Science, Western University, London, N6A 3K7, Canada. Charles.Ling@uwo.ca.
  • Qi Zhang
    Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.

Keywords

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