Liver fibrosis classification on trichrome histology slides using weakly supervised learning in children and young adults
CLAM pipeline showed promise in detecting features important for differentiating low- and high-stage fibrosis from trichrome WSI based on the results, offering a promising objective method for liver fibrosis detection in children and young adults.
Authors
- Elanchezhian Somasundaram (ORCID: https://orcid.org/0000-0002-0440-5238)
- Jonathan R. Dillman (ORCID: https://orcid.org/0000-0003-0124-0164)
- Zachary L. Taylor (ORCID: https://orcid.org/0000-0001-5178-2397)
- Anas Bernieh
- Jennifer Picarsic (ORCID: https://orcid.org/0000-0002-3718-6422)
- Sarangarajan Ranganathan (ORCID: https://orcid.org/0000-0001-6214-9167)
- Christopher Woods (ORCID: https://orcid.org/0000-0002-9776-4474)
- Mahdieh Shabanian (ORCID: https://orcid.org/0000-0002-2133-2069)
- Lili He
Institutions
- Cincinnati Children's Hospital Medical Center (US)
- University of Utah (US)
- Children's Hospital of Pittsburgh (US)
- University of Cincinnati (US)
- University of Cincinnati Medical Center (US)
Publication Details
- Journal
- Journal of Pathology Informatics
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1016/j.jpi.2024.100416
- Citations
- 4
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- Cincinnati Children's Hospital Medical Center
- National Institutes of Health