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

2026-09-18

AI Summary

为解决医学图像分析中多标签少样本学习的问题，提出Prototype Purification and Regulation框架，通过净化原型并调整类间相似性来提高疾病检测准确性。

Abstract

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. PPR first performs prototype purification by leveraging sample-level comorbidity scores to emphasize disease-specific features, producing purified prototypes that better characterize each disease. Building upon these purified prototypes, PPR further addresses the underexplored problem of inter-class prototype distance in MIA by incorporating disease-level comorbidity statistics to adaptively regulate inter-class similarity, forming a comorbidity-aware embedding space. Overall, PPR sequentially enables the model to capture pure disease features and inter-class relationships for reliable MLFSL in MIA. Extensive experiments across four chest X-ray benchmark datasets, including cross-domain evaluation, show that PPR consistently outperforms state-of-the-art methods, significantly improving disease detection while demonstrating robust generalization and clinical applicability.

Problem

• Multi-label Few-Shot Learning
• Medical Image Analysis
• Prototype Purification
• Comorbidity
• Inter-class Similarity

Innovation

• Prototype Purification and Regulation
• Comorbidity-Aware
• Multi-Label Few-Shot Learning
• Medical Image Analysis