arXiv AI By Canyu Lei, Fenglin Zhang, Derek Bivona, Cristiane Singulane, Jonathan Pan, Kenneth Bilchick, Amit R. Patel, Jianxin Xie

MSAIC-Net: A Multi-Scale Attention and Imbalance-Aware Contrastive Network for ECG-Based Myocardial Substrate Abnormality Detection

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arXiv:2606. 06718v1 Announce Type: cross Abstract: Myocardial substrate abnormalities, such as myocardial scar and myocardial infarction (MI), are associated with adverse cardiovascular outcomes.

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arXiv AI
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Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs

arXiv:2607. 20814v1 Announce Type: new Abstract: The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained by limited interpretability and the hallucination risk of large language models (LLMs).

By Hai-Nam Duy Vuong, Duy-Anh Bui, Trong-Nghia Nguyen, Kim-Ngan Thi Nguyen, Trang Mai Xuan, Tien-Cuong Nguyen, Van-Dem Pham, Thien Van Luong
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HTC-SGA Former: A Hybrid Transformer-CNN Network with Self-Guided Attention and a New Boundary-Weighted Adaptive Loss for Coronary DSA Vessel Segmentation

Accurate coronary Digital Subtraction Angiography (DSA) vessel segmentation is essential for computer-aided diagnosis and treatment planning of coronary artery disease (CAD). However, thin low-contrast vessels, background interference, and severe vessel-background class imbalance make reliable segmentation of weak distal branches and vessel boundaries challenging.