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Protoecgnet: Case-Based Interpretable Deep Learning For Multi-Label Ecg Classification With Contrastive Learning by kenlee.reb is a document available to read on EtoBox.

What is Protoecgnet: Case-Based Interpretable Deep Learning For Multi-Label Ecg Classification With Contrastive Learning about?

The document introduces ProtoECGNet, a prototype-based deep learning model designed for interpretable multi-label ECG classification, addressing the limitations of traditional black-box models in clinical settings. It employs a structured architecture with multiple prototype branches to reflect clinical reasoning and utilizes a novel contrastive loss to improve prototype quality and classification performance. Evaluations demonstrate that ProtoECGNet provides competitive results while offering clear, case-b

Author

kenlee.reb

Language

EN