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

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
  3. Kunhoth S, Al-maadeed S, Akbari Y, Al Saady R. Computational Methods for Breast Cancer Molecular Profiling using Routine Histopathology: A Review. Archives of Computational Methods in Engineering 2025 View
  4. Tchokponhoue G, Idri A. On the value of uncertainty quantification in deep learning based breast cancer molecular subtype classification. Applied Soft Computing 2026;186:114249 View

Books/Policy Documents

  1. Mohamed H, Toumi A. Progress in Intelligent Computing and Secure Communication Systems. View