解决呼吸音分类中患者过拟合与多周期混合偏差问题
PC-MCL: Patient-Consistent Multi-Cycle Learning with multi-label bias correction for respiratory sound classification
- 采用多周期拼接+三标签（正常/爆裂音/哮鸣音）设计
- 在ICBHI 2017上达到65.37%的ICBHI评分，优于基线
- 适合临床呼吸音分析与跨患者泛化场景
自动化呼吸音分类有助于肺部疾病诊断，但现有深度模型多依赖周期级分析，易产生患者特异性过拟合。本文提出PC-MCL（患者一致性多周期学习），通过三个核心组件：多周期拼接、三标签建模和患者匹配辅助任务，解决呼吸音分类中的多标签分布偏差问题。该偏差在传统双标签（爆裂音、哮鸣音）下表现为正常与异常周期混合时正常信号信息丢失。本文提出的三标签（正常、爆裂音、哮鸣音）能保留混合样本中各周期的信息。此外，患者匹配辅助任务作为多任务正则项，提升模型鲁棒性与泛化能力。在ICBHI 2017基准上，PC-MCL取得65.37%的ICBHI评分，优于现有方法。消融实验表明三者协同作用，对异常呼吸事件检测至关重要。
原文摘要 · Abstract (English)
Automated respiratory sound classification supports the diagnosis of pulmonary diseases. However, many deep models still rely on cycle-level analysis and suffer from patient-specific overfitting. We propose PC-MCL (Patient-Consistent Multi-Cycle Learning) to address these limitations by utilizing three key components: multi-cycle concatenation, a 3-label formulation, and a patient-matching auxiliary task. Our work resolves a multi-label distributional bias in respiratory sound classification, a critical issue inherent to applying multi-cycle concatenation with the conventional 2-label formulation (crackle, wheeze). This bias manifests as a systematic loss of normal signal information when normal and abnormal cycles are combined. Our proposed 3-label formulation (normal, crackle, wheeze) corrects this by preserving information from all constituent cycles in mixed samples. Furthermore, the patient-matching auxiliary task acts as a multi-task regularizer, encouraging the model to learn more robust features and improving generalization. On the ICBHI 2017 benchmark, PC-MCL achieves an ICBHI Score of 65.37%, outperforming existing baselines. Ablation studies confirm that all three components are essential, working synergistically to improve the detection of abnormal respiratory events.
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