Deep feature batch correction using ComBat for machine learning applications in computational pathology
ComBat harmonization of deep learning-derived histology features effectively reduces the risk of AI models learning confounding features in WSIs, ensuring more reliable performance estimates. This approach is promising for the integration of large-scale digital pathology datasets.
Authors
- Orla Sheils (ORCID: https://orcid.org/0000-0002-4493-9496)
- Stephen P. Finn (ORCID: https://orcid.org/0000-0002-8628-5814)
- Pierre Murchan
- Pilib Ó Broin (ORCID: https://orcid.org/0000-0002-6702-8564)
- Anne‐Marie Baird (ORCID: https://orcid.org/0000-0001-7501-1159)
Institutions
- Trinity College Dublin (IE)
Publication Details
- Journal
- Journal of Pathology Informatics
- Published
- 2024-09-13
- DOI
- https://doi.org/10.1016/j.jpi.2024.100396
- Citations
- 3
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- Science Foundation Ireland