This paper is in the following e-collection/theme issue:

Breast Cancer (206) Clinical Informatics (1022) Clinical Information and Decision Making (1407) Machine Learning (1465) Decision Support for Health Professionals (1185) Innovations and Technology in Cancer Care (423) Artificial Intelligence (1371)

Published on in Vol 9 (2023)

This is a member publication of National University of Singapore

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45547, first published .
Data-Efficient Computational Pathology Platform for Faster and Cheaper Breast Cancer Subtype Identifications: Development of a Deep Learning Model

Data-Efficient Computational Pathology Platform for Faster and Cheaper Breast Cancer Subtype Identifications: Development of a Deep Learning Model

Data-Efficient Computational Pathology Platform for Faster and Cheaper Breast Cancer Subtype Identifications: Development of a Deep Learning Model

Authors of this article:

Kideog Bae1 Author Orcid Image ;   Young Seok Jeon2 Author Orcid Image ;   Yul Hwangbo1, 3 Author Orcid Image ;   Chong Woo Yoo4 Author Orcid Image ;   Nayoung Han1, 3, 4 Author Orcid Image ;   Mengling Feng5 Author Orcid Image
Article Authors Cited by (1) Tweetations (4) Metrics

Kideog Bae   1 * , PhD ;   Young Seok Jeon   2 * , PhD ;   Yul Hwangbo   1, 3 , MD, PhD ;   Chong Woo Yoo   4 , MD, PhD ;   Nayoung Han   1, 3, 4 * , MD, PhD ;   Mengling Feng   5 * , PhD

1 Healthcare AI Team, Healthcare Platform Center, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea

2 Institute of Data Science, National University of Singapore, Singapore, Singapore

3 Department of Cancer AI & Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea

4 Department of Pathology, National Cancer Center Hospital, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea

5 Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

*these authors contributed equally

Corresponding Author:

  • Mengling Feng, PhD
  • Saw Swee Hock School of Public Health
  • National University of Singapore
  • 12 Science Drive 2
  • Tahir foundation MD1 #09-01
  • Singapore, 117549
  • Singapore
  • Phone: 65 65164984
  • Email: ephfm@nus.edu.sg