Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer.

Journal: Cancer research and treatment
Published Date:

Abstract

PURPOSE: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin-stained frozen tissue sections of SLNs in 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.
  • Hyunna Lee
    Department of Radiology, Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, 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.
  • 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.
  • Dongho Shin
    Department of Radiation Oncology, Proton Therapy Center, National Cancer Center, 323, Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do, 10408, South Korea.
  • Yeonsoo Yoo
    KakaoBrain-BrainCloud Team, Seongnam, Korea.
  • Kyowoon Lee
    Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, Korea.
  • Dahye Kim
    Image Laboratory, School of Computer Science and Engineering, ChungAng University, Seoul, Korea.
  • Hwejin Jung
    Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea.
  • Hyunbin Cho
    DoAI Inc., Seoul, Korea.
  • Hyungyu Lee
    DoAI Inc., Seoul, Korea.
  • Taeu Kim
    Department of Business Management and Convergence Software, Sogang University, Seoul, Korea.
  • Jong Hyun Choi
    Data Science & Business Analytics Lab, School of Industrial Management Engineering, College of Engineering, Korea University, Seoul, Korea.
  • Changwon Seo
    DoAI Inc., Seoul, Korea.
  • Seong Il Han
    Software Graduate Program, School of Computing, College of Engineering, Korea Advanced Institute of Science and Technology, Seoul, Korea.
  • Young Je Lee
    Department of Biomedical Engineering, Yonsei University, Seoul, Korea.
  • Young Seo Lee
    Department of Social Studies Education, College of Education, Ewha Womans University, Seoul, Korea.
  • Hyung-Ryun Yoo
    Department of Math, University of Kwangwoon, Seoul, Korea.
  • Yongju Lee
    Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea.
  • Jeong Hwan Park
    Department of Pathology, Seoul National University College of Medicine and SMG-SNU Boramae Medical Center, Seoul, Korea.
  • Sohee Oh
    Department of Biostatistics, Seoul National University College of Medicine and SMG-SNU Boramae Medical Center, Seoul, Korea.
  • Gyungyub Gong
    Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.