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

Breast Cancer (247) Clinical Informatics (1168) Clinical Information and Decision Making (1665) Machine Learning (1748) Decision Support for Health Professionals (1347) Innovations and Technology in Cancer Care (466) Artificial Intelligence (1754)

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 (2) Tweetations (4) Metrics

Journals

  1. Salo I, Nordlund L, Eklund L, Ho J, Soini M, Kumar D, Yeong J, Guan F, Metsälä E. Advancements and applications of AI technologies in pathology: a scoping review. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2024;12(1) View
  2. Ma Y, Jamdade S, Konduri L, Sailem H. AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology. npj Digital Medicine 2025;8(1) View