Few-shot learning and explainable AI for colon cancer histopathology: A prototypical network with multi-technique interpretability

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.