International journal of medical informatics · 2025
Few-shot learning and explainable AI for colon cancer histopathology: A prototypical network with multi-technique interpretability.
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Merabet Asma, Saighi Asma, Laboudi Zakaria, Abderraouf Ferradji Mohamed, Harous Saad, Wagdy Mohamed Ali, Mousavirad Seyed Jalaleddin, Almazyad Abdulaziz S
Аннотация
BACKGROUND: Colon cancer diagnosis from histopathology is challenging due to limited annotated data and the lack of interpretability in deep models.
OBJECTIVE: We present a data-efficient framework combining few-shot learning and explainable AI for accurate and transparent diagnosis.
METHODS: A Prototypical Network with a ConvNeXt-Tiny backbone was trained on small colon-tissue image sets. Explanations from Grad-CAM and LIME were validated by a pathologist, and generalization was tested on an external dataset.
RESULTS: The model achieved 98.5 % accuracy in-domain and 90 % on the EBHI dataset, showing strong generalization.
CONCLUSIONS: 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.
OBJECTIVE: We present a data-efficient framework combining few-shot learning and explainable AI for accurate and transparent diagnosis.
METHODS: A Prototypical Network with a ConvNeXt-Tiny backbone was trained on small colon-tissue image sets. Explanations from Grad-CAM and LIME were validated by a pathologist, and generalization was tested on an external dataset.
RESULTS: The model achieved 98.5 % accuracy in-domain and 90 % on the EBHI dataset, showing strong generalization.
CONCLUSIONS: 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.