CardioThink applies structured clinical reasoning stages and Structured Set Policy Optimization (SSPO) to ECG classification, yielding higher diagnostic accuracy and more interpretable rationales than direct prediction baselines on multiple benchmarks.
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ECG-NAT combines masked autoencoder pretraining with hierarchical neighborhood attention and dual-loss fine-tuning to reach 88.1% accuracy on ECG classification using just 1% labeled data.
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Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification
CardioThink applies structured clinical reasoning stages and Structured Set Policy Optimization (SSPO) to ECG classification, yielding higher diagnostic accuracy and more interpretable rationales than direct prediction baselines on multiple benchmarks.
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ECG-NAT: A Self-supervised Neighborhood Attention Transformer for Multi-lead Electrocardiogram Classification
ECG-NAT combines masked autoencoder pretraining with hierarchical neighborhood attention and dual-loss fine-tuning to reach 88.1% accuracy on ECG classification using just 1% labeled data.