解决心电图分类中未见异常导致的错误标注问题。
SafeECGMatch: Calibration-Aware Joint Frequency and Time Space Semi-Supervised Learning for Open-Set ECG Classification
- 双分支结构提取时频特征，动态校准置信度与实际准确率。
- 在PTB-XL和CinC挑战赛上达到最佳精度与校准性能。
- 适合临床场景下标签不全、存在未知异常的心电图分析。
心电图（ECG）分类模型常面临标签稀缺问题，半监督学习（SSL）可降低标注成本。但在临床中，未标注数据常包含分布外（OOD）异常或标签集中未涵盖的诊断类别。标准SSL会将错误伪标签强加于这些未知类，导致预测过度自信。为此，本文提出SafeECGMatch，一种针对单标签ECG分类的校准感知安全半监督框架，应对标签分布不匹配问题。方法上，该框架采用双分支结构，通过心电图特有增强提取时频潜在表示；关键在于，通过自适应标签平滑与温度缩放，动态对齐置信度与经验准确率，联合校准多类分类器与分布外检测器，覆盖时间与频谱域。这种联合优化实现可信的分布外拒绝与可靠伪标签生成。在PTB-XL与PhysioNet/CinC Challenge数据集上评估，SafeECGMatch取得当前最优精度与校准表现，推动生理时序数据中可靠知识发现。代码已公开于https://github.com/labhai/SafeECGMatch。
原文摘要 · Abstract (English)
Electrocardiogram (ECG) classification models often suffer from severe label scarcity, making semi-supervised learning (SSL) an attractive strategy for reducing annotation costs. In clinical settings, however, unlabeled pools frequently contain out-of-distribution (OOD) anomalies or diagnostic groups absent from the labeled set. Standard SSL forces incorrect pseudo-labels onto these unseen classes, producing overconfident predictions. To address this, we propose SafeECGMatch, a calibration-aware safe SSL framework for single-label ECG classification under label distribution mismatch. Methodologically, SafeECGMatch employs a dual-branch architecture extracting time-frequency latent representations via ECG-specific augmentations. Crucially, it dynamically aligns confidence with empirical accuracy through adaptive label smoothing and temperature scaling, calibrating both the multiclass classifier and the OOD detector across temporal and spectral domains. This joint optimization allows trustworthy OOD rejection and reliable pseudo-labeling. Evaluated on the PTB-XL and PhysioNet/CinC Challenge benchmarks, SafeECGMatch achieves state-of-the-art accuracy and calibration, advancing reliable knowledge discovery in physiological time-series. Code is available at https://github.com/labhai/SafeECGMatch.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。