通过患者自适应特征对齐，提升肺音分类鲁棒性
Patient-Aware Feature Alignment for Robust Lung Sound Classification:Cohesion-Separation and Global Alignment Losses
- 设计双损失机制，分别强化同患者内聚与跨患者分离
- 在ICBHI数据集上达72.08%两分类准确率，显著优于基线
- 适合需要个性化医疗分析的临床肺音诊断场景
肺音分类对呼吸系统疾病早期诊断至关重要。然而，即使症状相同，不同患者的生物信号也存在显著差异，需考虑个体差异的学习方法。本文提出患者自适应特征对齐（PAFA）框架，引入两种新损失：患者内聚-分离损失（PCSL）和全局患者对齐损失（GPAL）。PCSL使同一患者的特征聚集，同时分离不同患者特征，以捕捉个体差异；GPAL将每位患者的特征中心向全局中心拉近，防止特征空间碎片化。在ICBHI数据集上，该方法在四分类任务中取得64.84%准确率，在两分类任务中达72.08%。结果表明，PAFA能有效捕捉个体化模式，并在不同患者群体中实现性能提升，为以患者为中心的医疗应用提供支持。
原文摘要 · Abstract (English)
Lung sound classification is vital for early diagnosis of respiratory diseases. However, biomedical signals often exhibit inter-patient variability even among patients with the same symptoms, requiring a learning approach that considers individual differences. We propose a Patient-Aware Feature Alignment (PAFA) framework with two novel losses, Patient Cohesion-Separation Loss (PCSL) and Global Patient Alignment Loss (GPAL). PCSL clusters features of the same patient while separating those from other patients to capture patient variability, whereas GPAL draws each patient's centroid toward a global center, preventing feature space fragmentation. Our method achieves outstanding results on the ICBHI dataset with a score of 64.84\% for four-class and 72.08\% for two-class classification. These findings highlight PAFA's ability to capture individualized patterns and demonstrate performance gains in distinct patient clusters, offering broader applications for patient-centered healthcare.
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