解决睡眠分期中多源数据噪声标签下的模型泛化问题
Towards Multi-Source Domain Generalization for Sleep Staging with Noisy Labels
- 提出联合时频早期正则化机制，提升多模态信号一致性
- 在五大数据集上实现噪声环境下最优性能，超越现有方法
- 首个针对噪声标签多源域泛化的睡眠分期基准，适合医疗AI研究者
自动睡眠分期是涉及脑电图（EEG）和眼电图（EOG）等异构生理信号的多模态学习任务，常受机构、设备和人群间域偏移影响。实际数据还存在标注噪声，但噪声标签与多源域泛化共存场景下仍缺乏有效方法。本文首次构建了噪声标签多源域泛化睡眠分期（NL-DGSS）基准，并发现现有噪声标签学习方法在域偏移与标签噪声并存时性能显著下降。为此，我们提出FF-TRUST框架，通过联合时间-频率早期学习正则化（JTF-ELR）与置信度多样性正则化，增强模型在噪声监督下的域不变性。在五个公开数据集上的实验表明，该方法在对称与非对称噪声设置下均达到一致的最先进性能。代码与基准已开源。
原文摘要 · Abstract (English)
Automatic sleep staging is a multimodal learning problem involving heterogeneous physiological signals such as EEG and EOG, which often suffer from domain shifts across institutions, devices, and populations. In practice, these data are also affected by noisy annotations, yet label-noise-robust multi-source domain generalization remains underexplored. We present the first benchmark for Noisy Labels in Multi-Source Domain-Generalized Sleep Staging (NL-DGSS) and show that existing noisy-label learning methods degrade substantially when domain shifts and label noise coexist. To address this challenge, we propose FF-TRUST, a domain-invariant multimodal sleep staging framework with Joint Time-Frequency Early Learning Regularization (JTF-ELR). By jointly exploiting temporal and spectral consistency together with confidence-diversity regularization, FF-TRUST improves robustness under noisy supervision. Experiments on five public datasets demonstrate consistent state-of-the-art performance under diverse symmetric and asymmetric noise settings. The benchmark and code will be made publicly available at https://github.com/KNWang970918/FF-TRUST.git.
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