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Purification and Regulation: Comorbidity-Aware Multi-Label Few-Shot Learning for Medical Image Classification

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

Multi-label few-shot learning (MLFSL) remains a significant challenge in medical image analysis (MIA). Current metric-based meta-learning methods face two critical limitations in MIA. First, conventional prototype generation often entangles irrelevant disease information, leading to contaminated prototypes and degraded performance. Second, prior studies typically enforce inter-class separability in embedding space, largely neglecting the inherent correlations among diseases. To overcome these challenges, we propose Prototype Purification and Regulation (PPR), a novel MLFSL framework for MIA. P

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