ProtoECGNet：基于案例的可解释深度学习用于多标签心电图分类与对比学习
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
AI总结：
ProtoECGNet提出基于原型的可解释多标签心电图分类模型，通过多分支架构和对比损失，在PTB-XL上达到竞争性能，提供忠实案例解释。
摘要：
基于深度学习的心电图（ECG）分类已展现出令人瞩目的性能，但由于缺乏透明且忠实的解释，其临床采用进展缓慢。诸如显著性图等事后方法可能无法反映模型的真实决策过程。基于原型（prototype）的推理提供了一种更透明的替代方案，通过将决策基于与真实心电图片段学习表示的相似性，从而实现忠实、基于案例的解释。我们提出了ProtoECGNet，一种基于原型的深度学习模型，用于可解释的多标签心电图分类。ProtoECGNet采用一种反映临床解读工作流程的结构化多分支架构：它集成了带有全局原型的1D CNN用于节律分类，带有时间局部化原型的2D CNN用于基于形态的推理，以及带有全局原型的2D CNN用于弥漫性异常。每个分支都使用专为多标签学习设计的原型损失进行训练，该损失结合了聚类、分离、多样性以及一种新颖的对比损失，该对比损失鼓励不相关类别的原型之间进行适当分离，同时允许频繁共现的诊断进行聚类。我们在PTB-XL数据集的所有71个诊断标签上评估了ProtoECGNet，展示了相对于最先进黑盒模型的竞争性能，同时提供了结构化的、基于案例的解释。为了评估原型质量，我们对最终模型投影的原型进行了结构化的临床医生审查，发现它们被评为具有代表性和清晰性。ProtoECGNet表明，原型学习可以有效地扩展到复杂的多标签时间序列分类，为临床决策支持提供了一条通往透明且可信的深度学习模型的实用路径。
英文摘要：
Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of transparent and faithful explanations. Post hoc methods such as saliency maps may fail to reflect a model's true decision process. Prototype-based reasoning offers a more transparent alternative by grounding decisions in similarity to learned representations of real ECG segments, enabling faithful, case-based explanations. We introduce ProtoECGNet, a prototype-based deep learning model for interpretable, multi-label ECG classification. ProtoECGNet employs a structured, multi-branch architecture that reflects clinical interpretation workflows: it integrates a 1D CNN with global prototypes for rhythm classification, a 2D CNN with time-localized prototypes for morphology-based reasoning, and a 2D CNN with global prototypes for diffuse abnormalities. Each branch is trained with a prototype loss designed for multi-label learning, combining clustering, separation, diversity, and a novel contrastive loss that encourages appropriate separation between prototypes of unrelated classes while allowing clustering for frequently co-occurring diagnoses. We evaluate ProtoECGNet on all 71 diagnostic labels from the PTB-XL dataset, demonstrating competitive performance relative to state-of-the-art black-box models while providing structured, case-based explanations. To assess prototype quality, we conduct a structured clinician review of the final model's projected prototypes, finding that they are rated as representative and clear. ProtoECGNet shows that prototype learning can be effectively scaled to complex, multi-label time-series classification, offering a practical path toward transparent and trustworthy deep learning models for clinical decision support.
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