PaperarXiv:2610.28083
IECG: Weakly-Supervised Interpretability-Enhancing Foundation Model for Electrocardiogram
WACV 2027 (accepted, per arXiv comment); arXiv 2610.28083v1 (cs.AI), submitted 2026-10-20, announced 2026-10-21
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- Form factor: clinical
Abstract
Electrocardiography (ECG) is a cornerstone of cardiac diagnosis, yet most machine learning methods for ECG analysis lack interpretability, limiting their clinical adoption. We present IECG, a weakly-supervised ECG foundation model that jointly performs diagnostic classification and abnormality localization without requiring manual abnormal region annotations, overcoming a long-standing bottleneck in interpretable ECG learning. Central to IECG is a weakly-supervised attention strategy that explicitly incorporates clinical priors by deriving abnormality heatmap pseudo-labels from established ECG clinical knowledge. These pseudo-labels guide the model's CLS-token attention through a KL-divergence loss, encouraging it to focus on diagnostically relevant waveform regions. During pretraining, IECG is jointly optimized with clinically guided attention supervision, ECG-text contrastive alignment, and report captioning objectives, enabling it to learn rich multimodal representations while producing localized waveform evidence. By explicitly aggregating features from clinically guided attention regions, IECG grounds its diagnostic predictions in interpretable waveform evidence without sacrificing classification performance. Extensive experiments demonstrate that IECG achieves state-of-the-art abnormal region localization while maintaining strong classification performance, advancing trustworthy foundation models for ECG analysis.
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APA
Nguyen, H. D., Pham, T.-T., Do, A.-T., Dinh, P.-N., Patel, B., Le, N., & Nguyen, V. (2026). IECG: Weakly-Supervised Interpretability-Enhancing Foundation Model for Electrocardiogram. WACV 2027. https://arxiv.org/abs/2610.28083
BibTeX
@article{nguyen2026iecg,
title = {{IECG: Weakly-Supervised Interpretability-Enhancing Foundation Model for Electrocardiogram}},
author = {Nguyen, Huynh Dang and Pham, Trong-Thang and Do, Anh-Thuan and Dinh, Phuong-Nhi and Patel, Brijesh and Le, Ngan and Nguyen, Van},
year = {2026},
journal = {WACV 2027},
eprint = {2610.28083},
archivePrefix = {arXiv}
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