The paper introduces LYNX-ECG, a graph neural network that represents each heartbeat as a sparse visibility graph of timestamped turning points, enabling accurate ECG classification while providing auditable, clinically grounded explanations. On the CODE-Test dataset, LYNX-ECG achieves a macro F1 score of 0.889, outperforming five baselines including the ECGFounder foundation model, and its graphs require only about 14% of the storage needed for full-sample graphs. Because each graph node corresponds to a specific ECG time, clinicians can delete waveform regions or alter beat timing to audit predictions, with the clinically expected region proving strongest evidence in over 90% of correct conduction disorder predictions.
"whyItMatters":"The model’s ability to pair high classification accuracy with transparent, testable explanations directly addresses the need for clinically interpretable AI in ECG analysis."
By Artur Xavier, Guilherme Henrique Gomes Evangelista, Aitazaz Khan, Antonio Luiz Pinho Ribeiro, Mohammed J. Zaki, Srinivasan Parthasarathy, Wagner Meira Jr
The paper introduces Graph-CMMC, a graph-based pseudo‑multimodal contrastive learning framework for 12‑lead ECG representations. It transforms ECG waveforms into Gramian Angular Difference Field (GADF) images to create complementary views, then aligns these views while modeling inter‑lead dependencies with a graph module. Experiments on coronary artery occlusion classification show that Graph-CMMC performs competitively with supervised methods, highlighting the value of GADF representations and explicit graph modeling for robust ECG analysis.
By Mengyu Wang, Kozo Okada, Takafumi Goto, Natsuko Jinba, Hiroki Yamaya, Kiyoshi Hibi, Tomoki Hamagami
arXiv:2512. 13765v2 Announce Type: replace-cross Abstract: The forward problem in electrocardiology, computing body surface potentials from cardiac electrical activity, is traditionally solved using physics-based models such as the bidomain or monodomain equations.
By Shaheim Ogbomo-Harmitt, Cesare Magnetti, Chiara Spota, Jakub Grzelak, Oleg Aslanidi
arXiv:2606. 14828v1 Announce Type: cross Abstract: Leptomeningeal collaterals (LMCs) are an important prognostic factor in acute ischemic stroke.
By Junyong Cao, Hakim Baazaoui, Chinmay Prabhakar, Suprosanna Shit, Lukas Bastian Otto, Susanne Wegener, Bjoern Menze, Ezequiel de la Rosa
arXiv:2607. 12054v1 Announce Type: cross Abstract: Breast ultrasound is widely used for screening, yet automated analysis remains challenging due to speckle noise, acquisition variability, and weak separation of benign and malignant cases in standard ultrasound imaging.
By Sabahattin Mert Daloglu, Ceren Coskun, Harvey Castro, Soner Hacihaliloglu, Ilker Hacihaliloglu
The paper presents an anatomy‑aligned surface learning framework for reconstructing patient‑specific 4D myocardial surfaces from sparsely sampled short‑axis cine MRI. By parameterizing epicardial and endocardial surfaces on a shared circumferential‑longitudinal UV domain, the method transforms irregular 3D reconstruction into structured coordinate‑field completion, enabling explicit correspondence across subjects and cardiac phases. Experiments on three public datasets show the approach outperforms mesh‑based and implicit methods, achieving Chamfer distances around 2.6–2.9 mm and preserving ventricular function with small errors in volume and ejection fraction.
By Xiaohan Yuan, Xuan Yang, Qingya Li, Yangang Wang, Lei Li