首个心电图半监督分割基准，推动标注稀缺下的智能诊断
SemiSegECG: A Multi-Dataset Benchmark for Semi-Supervised Semantic Segmentation in ECG Delineation
- 构建多数据集统一基准，整合未充分利用的公开心电图数据
- 变压器模型在半监督分割中优于卷积网络，跨域表现更稳定
- 提供标准化训练与评估流程，适合医疗AI研究者参考
心电图辨识（ECG delineation）是识别有意义波形特征的关键步骤，对临床诊断至关重要。尽管深度学习取得进展，但受限于公开标注数据集稀缺。半监督学习通过利用大量未标注心电图数据提供了新路径。本文提出SemiSegECG，首个针对心电图语义分割的半监督学习系统性基准。我们整合多个公开数据集，包括此前较少使用的来源，以支持稳健且多样化的评估。采用计算机视觉领域五种代表性半监督算法，在卷积网络与变换器两种架构上，分别在同域与跨域设置下进行测试。同时提出适配心电图的训练配置与增强策略，并建立标准化评估框架。结果显示，变换器在半监督心电图分割任务中表现优于卷积网络。SemiSegECG有望成为推进半监督心电图分割方法的基础平台，促进该领域进一步研究。
原文摘要 · Abstract (English)
Electrocardiogram (ECG) delineation, the segmentation of meaningful waveform features, is critical for clinical diagnosis. Despite recent advances using deep learning, progress has been limited by the scarcity of publicly available annotated datasets. Semi-supervised learning presents a promising solution by leveraging abundant unlabeled ECG data. In this study, we present SemiSegECG, the first systematic benchmark for semi-supervised semantic segmentation (SemiSeg) in ECG delineation. We curated and unified multiple public datasets, including previously underused sources, to support robust and diverse evaluation. We adopted five representative SemiSeg algorithms from computer vision, implemented them on two different architectures: the convolutional network and the transformer, and evaluated them in two different settings: in-domain and cross-domain. Additionally, we propose ECG-specific training configurations and augmentation strategies and introduce a standardized evaluation framework. Our results show that the transformer outperforms the convolutional network in semi-supervised ECG delineation. We anticipate that SemiSegECG will serve as a foundation for advancing semi-supervised ECG delineation methods and will facilitate further research in this domain.
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