MAR-ECG is an ontology-guided masked autoregressive pretraining method that uses SNOMED-CT graph alignment and graph-smoothed contrastive learning plus signal-derived auxiliaries to achieve competitive ECG classification performance without clinical text.
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Contrastive predictive coding pretraining combined with structured state space models yields the strongest ECG foundation models, with continued gains from scaling data to 11 million samples.
ProtoSSL discovers generalizable prototypes from unlabeled time-series via self-supervision and assigns them to new tasks for interpretable predictions, outperforming supervised baselines in low-data regimes on ECG datasets.
A graph-based ECG synthesis framework using eikonal-template and pseudo-diffusion reaction-eikonal backends with Bellman residual activation certification and a two-stage diagnostics-aware curation pipeline.
Empirical scaling study of ECG models finds SSL scales robustly while ResNets show 1.3-2.5x better parameter efficiency and SSL up to 16x better data efficiency than supervised baselines on out-of-distribution tasks.
An unsupervised method detects domain shifts via localized density anomaly search in feature space, attributes the shift to a minimal subspace, and extracts balanced subsets from two unlabeled datasets.
Proposes MedRLM, a recursive agent-based multimodal framework for long-context clinical reasoning, sensor-guided screening, and referral optimization using a Clinical Evidence Graph Memory.
HeartBeatAI reports 98% Macro F1 under intra-source testing on four ECG datasets but shows significant degradation on rare anomalies under leave-one-domain-out evaluation.
citing papers explorer
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From Reports to Ontologies: Ontology-Guided Representation Learning for 12-Lead ECG
MAR-ECG is an ontology-guided masked autoregressive pretraining method that uses SNOMED-CT graph alignment and graph-smoothed contrastive learning plus signal-derived auxiliaries to achieve competitive ECG classification performance without clinical text.
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Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study
Contrastive predictive coding pretraining combined with structured state space models yields the strongest ECG foundation models, with continued gains from scaling data to 11 million samples.
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ProtoSSL: Interpretable Prototype Learning from Unlabeled Time-Series Data
ProtoSSL discovers generalizable prototypes from unlabeled time-series via self-supervision and assigns them to new tasks for interpretable predictions, outperforming supervised baselines in low-data regimes on ECG datasets.
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Graph-Based ECG Synthesis with Activation-Consistency Certification and Diagnostics-Aware Morphology Curation
A graph-based ECG synthesis framework using eikonal-template and pseudo-diffusion reaction-eikonal backends with Bellman residual activation certification and a two-stage diagnostics-aware curation pipeline.
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Unsupervised Domain Shift Detection with Interpretable Subspace Attribution
An unsupervised method detects domain shifts via localized density anomaly search in feature space, attributes the shift to a minimal subspace, and extracts balanced subsets from two unlabeled datasets.
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MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization
Proposes MedRLM, a recursive agent-based multimodal framework for long-context clinical reasoning, sensor-guided screening, and referral optimization using a Clinical Evidence Graph Memory.
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HeartBeatAI: An Interpretable and Robust Deep Learning Framework for Multi-Label ECG Arrhythmia Detection
HeartBeatAI reports 98% Macro F1 under intra-source testing on four ECG datasets but shows significant degradation on rare anomalies under leave-one-domain-out evaluation.