Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysis

• URL: http://arxiv.org/abs/2601.02409v1
• Date: Fri, 02 Jan 2026 05:09:35 GMT
• Title: Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysis
• Authors: Longwei Wang, Ifrat Ikhtear Uddin, KC Santosh,
• Abstract summary: Expert-Guided Explainable Few-Shot Learning and Explainability-Guided AL are presented.<n>EGxFSL integrates radiologist-defined regions-of-interest as spatial supervision via Grad-CAM-based Dice loss.<n>xGAL introduces iterative sample acquisition prioritizing both predictive uncertainty and attention misalignment.
• Score: 2.7946918847372277
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical image analysis faces two critical challenges: scarcity of labeled data and lack of model interpretability, both hindering clinical AI deployment. Few-shot learning (FSL) addresses data limitations but lacks transparency in predictions. Active learning (AL) methods optimize data acquisition but overlook interpretability of acquired samples. We propose a dual-framework solution: Expert-Guided Explainable Few-Shot Learning (EGxFSL) and Explainability-Guided AL (xGAL). EGxFSL integrates radiologist-defined regions-of-interest as spatial supervision via Grad-CAM-based Dice loss, jointly optimized with prototypical classification for interpretable few-shot learning. xGAL introduces iterative sample acquisition prioritizing both predictive uncertainty and attention misalignment, creating a closed-loop framework where explainability guides training and sample selection synergistically. On the BraTS (MRI), VinDr-CXR (chest X-ray), and SIIM-COVID-19 (chest X-ray) datasets, we achieve accuracies of 92\%, 76\%, and 62\%, respectively, consistently outperforming non-guided baselines across all datasets. Under severe data constraints, xGAL achieves 76\% accuracy with only 680 samples versus 57\% for random sampling. Grad-CAM visualizations demonstrate guided models focus on diagnostically relevant regions, with generalization validated on breast ultrasound confirming cross-modality applicability.

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