Prototype purification improves multi disease detection in chest x rays
Purification and Regulation: Comorbidity-Aware Multi-Label Few-Shot Learning for Medical Image Classification
Computer Vision and Pattern RecognitionMachine Learning
Summary
Identifying multiple diseases from medical images is hard, especially when only a few examples of each disease are available. The paper presents a new method called Prototype Purification and Regulation (PPR) that cleans up disease representations and respects how diseases often appear together. This helps the model recognize diseases more accurately and reliably. The authors tested PPR on chest X-ray tasks, showing it works better than existing methods even when applied to different datasets.
What this means in practice
- •For medical image analysis teams: Enhance disease detection in chest X-rays with improved prototype representations accounting for comorbidities using few training samples.
- •For clinical decision support developers: Integrate comorbidity-aware multi-label learning to improve diagnostic accuracy in automated medical image classification systems.
Authors
Ying-Chih Lin, Po-Chih Kuo, Yong-Sheng Chen
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.