基于上下文差异的对比学习：用于小样本场景下稳健的医学时间序列诊断
Contextual Discrepancy-Aware Contrastive Learning for Robust Medical Time Series Diagnosis in Small-Sample Scenarios
AI总结：
CoDAC通过上下文差异估计器和动态多视图对比框架，提升小样本场景下医学时间序列诊断的准确性和泛化能力。
摘要：
医学时间序列数据，如EEG和ECG，对于诊断神经和心血管疾病至关重要。然而，由于标注成本高，导致数据稀缺，以及传统对比学习在捕捉复杂时间模式方面的局限性，其精确解释面临重大挑战。为了解决这些问题，我们提出了CoDAC（基于上下文差异的对比学习），一种新的框架，旨在提高诊断准确性和泛化能力，特别是在小样本设置中。CoDAC利用外部健康数据，并引入基于Transformer的自动编码器构建的上下文差异估计器（CDE），通过上下文感知的异常评分精确量化异常信号。这些评分动态地指导动态多视图对比框架（DMCF），该框架自适应地加权不同的时间视图，以聚焦于诊断相关的、差异区域的对比学习。我们的编码器结合了扩张卷积和多头注意力，以实现稳健的特征提取。在阿尔茨海默病EEG、帕金森病EEG和心肌梗死ECG数据集上的全面实验表明，CoDAC在所有指标上均表现出色，一致优于最先进的基线方法，特别是在低标签可用性下。消融研究进一步验证了CDE和DMCF的关键贡献。CoDAC为医学时间序列诊断提供了稳健且可解释的解决方案，有效缓解了数据稀缺的挑战。
英文摘要：
Medical time series data, such as EEG and ECG, are vital for diagnosing neurological and cardiovascular diseases. However, their precise interpretation faces significant challenges due to high annotation costs, leading to data scarcity, and the limitations of traditional contrastive learning in capturing complex temporal patterns. To address these issues, we propose CoDAC (Contextual Discrepancy-Aware Contrastive learning), a novel framework that enhances diagnostic accuracy and generalization, particularly in small-sample settings. CoDAC leverages external healthy data and introduces a Contextual Discrepancy Estimator (CDE), built upon a Transformer-based Autoencoder, to precisely quantify abnormal signals through context-aware anomaly scores. These scores dynamically inform a Dynamic Multi-views Contrastive Framework (DMCF), which adaptively weights different temporal views to focus contrastive learning on diagnostically relevant, discrepant regions. Our encoder combines dilated convolutions with multi-head attention for robust feature extraction. Comprehensive experiments on Alzheimer's Disease EEG, Parkinson's Disease EEG, and Myocardial Infarction ECG datasets demonstrate CoDAC's superior performance across all metrics, consistently outperforming state-of-the-art baselines, especially under low label availability. Ablation studies further validate the critical contributions of CDE and DMCF. CoDAC offers a robust and interpretable solution for medical time series diagnosis, effectively mitigating data scarcity challenges.
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。
