Few-shot learning and explainable AI for colon cancer histopathology: A prototypical network with multi-technique interpretability
This few-shot and explainable model performs well with minimal data and generates clinically interpretable visual outputs, supporting its potential for reliable colon cancer diagnosis.
Authors
- Rahimah Zakaria (ORCID: https://orcid.org/0000-0002-2459-3213)
- Asma Saighi (ORCID: https://orcid.org/0000-0003-3790-2262)
- Seyed Jalaleddin Mousavirad (ORCID: https://orcid.org/0000-0001-8661-7578)
- Saad Harous (ORCID: https://orcid.org/0000-0001-6524-7352)
- Mohamed Abderraouf Ferradji
- Abdulaziz S. Almazyad (ORCID: https://orcid.org/0000-0003-4440-6208)
- Asma Merabet
- Ali Wagdy Mohamed (ORCID: https://orcid.org/0000-0002-5895-2632)
Institutions
- King Saud University (SA)
- University of Sharjah (AE)
- Larbi Ben M'hidi University of Oum El Bouaghi (DZ)
- University Ferhat Abbas of Setif (DZ)
- Zewail City of Science and Technology (EG)
- Mid Sweden University (SE)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2025-11-03
- DOI
- https://doi.org/10.1016/j.ijmedinf.2025.106167
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00