Molecular Classification of Breast Cancer Using Weakly Supervised Learning.

Journal: Cancer research and treatment
Published Date: Jun 25, 2024

Abstract

PURPOSE: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer.

Authors

  • Wooyoung Jang
    Department of Pathology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea.
  • Jonghyun Lee
    Civil and Environmental Engineering, University of Hawaii at Manoa, Honolulu, HI 96822, USA; Water Resources Research Center, University of Hawaii at Manoa, Hawaii, HI 96822, USA. Electronic address: jonghyun.harry.lee@hawaii.edu.
  • Kyong Hwa Park
    Division of Oncology/Hematology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea.
  • Aeree Kim
    Department of Pathology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea.
  • Sung Hak Lee
    Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, South Korea. hakjjang@catholic.ac.kr.
  • Sangjeong Ahn
    Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul National University Biomedical Informatics (SNUBI), Seoul, Republic of Korea. vanitasahn@gmail.com.

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