净化与调控：面向医学图像分类的共病感知多标签少样本学习
Purification and Regulation: Comorbidity-Aware Multi-Label Few-Shot Learning for Medical Image Classification
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- National Yang Ming Chiao Tung University（阳明交通大学）
- National Tsing Hua University（国立清华大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对医学图像多标签少样本学习中原型污染与忽视疾病相关性的问题，提出原型净化与调控（PPR）框架，利用共病评分净化原型并调控类间距离，在四个胸部X射线基准上持续超越现有方法。
中文摘要 AI 辅助
多标签少样本学习（MLFSL）在医学图像分析（MIA）中仍然是一个重大挑战。当前基于度量的元学习方法在MIA中面临两个关键局限。首先，传统的原型生成常常纠缠不相关的疾病信息，导致原型受到污染并降低性能。其次，先前的研究通常在嵌入空间中强制实现类间可分离性，而很大程度上忽略了疾病之间的固有相关性。为克服这些挑战，我们提出了原型净化与调控（PPR），一种用于MIA的新型MLFSL框架。PPR首先通过利用样本级共病评分来强调疾病特异性特征，从而执行原型净化，生成能更好刻画每种疾病的净化原型。在这些净化原型的基础上，PPR进一步通过引入疾病级共病统计量来自适应地调控类间相似性，解决了MIA中未被充分探索的类间原型距离问题，从而形成共病感知的嵌入空间。总体而言，PPR依次使模型能够捕获纯净的疾病特征和类间关系，以实现MIA中可靠的MLFSL。在四个胸部X射线基准数据集上的大量实验（包括跨域评估）表明，PPR持续优于最先进的方法，显著改善了疾病检测，同时展示了强大的泛化能力和临床适用性。
英文摘要
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.
