Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer.

Journal: Cancer research and treatment
Published Date: Sep 6, 2022

Abstract

PURPOSE: Assessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin-stained frozen tissue sections by pathologists is an essential but tedious and time-consuming task that contributes to accurate breast cancer staging. This study aimed to review a challenge competition (HeLP 2019) for the development of automated solutions for classifying the metastasis status of breast cancer patients.

Authors

  • Young-Gon Kim
    Department of Biomedical Engineering, Asan Institute of Life Science, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, South Korea.
  • In Hye Song
    Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
  • Seung Yeon Cho
    Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, Korea.
  • Sungchul Kim
    Department of Acupuncture & Moxibustion Medicine, Wonkwang University Gwangju Korean Medical Hospital, Gwangju, Korea; Nervous & Muscular System Disease Clinical Research Center of Wonkwang University Gwangju Korean Medical Hospital, Gwangju, Korea.
  • Milim Kim
    Department of Pathology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
  • Soomin Ahn
    Department of Pathology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Gyeonggi, 13620, Korea.
  • Hyunna Lee
    Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.
  • Dong Hyun Yang
    Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Namkug Kim
    Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Sungwan Kim
    Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Taewoo Kim
    Graduate School of Artificial Intelligence, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
  • Daeyoung Kim
  • Jonghyeon Choi
    Knowledge of AI Lab, NCSOFT, Seongnam, Korea.
  • Ki-Sun Lee
    Medical Science Research Center, Ansan Hospital, Korea University College of Medicine, Ansan, Korea.
  • Minuk Ma
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
  • Minki Jo
    School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
  • So Yeon Park
    Department of Pathology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Gyeonggi, 13620, Korea.
  • Gyungyub Gong
    Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

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