2 papers
cs.LG2025
Prototype Learning to Create Refined Interpretable Digital Phenotypes from ECGs
Sahil Sethi, David Chen, Michael C. Burkhart +3
Prototype-based neural networks offer interpretable predictions by comparing inputs to learned, representative signal patterns anchored in training data. While such models have sho…
cs.LG2025
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
Sahil Sethi, David Chen, Thomas Statchen +4
Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of transparent and faithful explanatio…