SafeECGMatch：面向开放集心电图分类的校准感知联合频率与时间空间半监督学习
SafeECGMatch: Calibration-Aware Joint Frequency and Time Space Semi-Supervised Learning for Open-Set ECG Classification
AI总结：
提出SafeECGMatch框架，通过双分支架构提取时频特征，结合自适应标签平滑和温度缩放校准模型，在标签分布不匹配下实现可靠的开集分类和OOD检测。
摘要：
心电图（ECG）分类模型常面临严重的标签稀缺问题，使得半监督学习（SSL）成为降低标注成本的有效策略。然而，在临床环境中，未标注数据池通常包含分布外（OOD）异常或标注集中不存在的诊断类别。标准SSL会强制对这些未见类别分配错误的伪标签，产生过度自信的预测。为解决此问题，我们提出SafeECGMatch，一个校准感知的安全SSL框架，用于标签分布不匹配下的单标签ECG分类。方法上，SafeECGMatch采用双分支架构，通过ECG特定的数据增强提取时频潜在表示。关键地，它通过自适应标签平滑和温度缩放动态对齐置信度与经验准确性，在时间和频谱域上校准多类分类器和OOD检测器。这种联合优化实现了可信的OOD拒绝和可靠的伪标签分配。在PTB-XL和PhysioNet/CinC Challenge基准上评估，SafeECGMatch达到了最先进的准确性和校准性能，推动了生理时间序列中可靠知识发现。代码可在https://github.com/labhai/SafeECGMatch获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
