arXiv Computer Vision
Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking
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arXiv Machine Learning
Sep 10
Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU
arXiv:2609.08992v1 Announce Type: new Abstract: False ventricular tachycardia (VT) alarms are a leading contributor to alarm fatigue in intensive care units. We propose a deep learning framework comb...
arXiv Computer Vision
3d ago
Echo-E$^3$Net: Efficient Endocardial Spatio-Temporal Network for Ejection Fraction Estimation
Echo-E$^3$Net is an anatomy‑guided spatio‑temporal neural network designed to estimate left ventricular ejection fraction (LVEF) from ultrasound images. It uses a dual‑phase Endocardial Border Detector to locate end‑diastole and end‑systole landmarks and an Endocardial Feature Aggregator to fuse these landmarks with global deep‑feature descriptors for EF regression. The model achieves competitive accuracy on EchoNet‑Dynamic and EchoNet‑Pediatric datasets while using only 1.55 M parameters and 8.05 GFLOPs, enabling real‑time deployment on limited‑resource devices.
arXiv AI
Jul 2
Learning Cardiac Motion Priors for Implicit Neural Representations
arXiv:2607. 00955v1 Announce Type: cross Abstract: Implicit neural representations (INRs) are well suited to cardiac motion estimation, providing continuous, compact representations of motion fields.
Hugging Face Trending Papers
Jun 30
Self-Supervised Temporal Regularization for Landmark-Based Cardiac Segmentation with Automatic AHA Regional Mapping
Graph-based cardiac segmentation with implicit anatomical correspondences provides topological guarantees and population-level analysis capabilities, but models trained on independent frames of image sequences exhibit temporal discontinuities that affect reliable clinical measurements, particularly in cardiac ultrasound. In this work, we introduce self-supervised temporal regularization as a post-training refinement stage that exploits the temporal coherence in image sequences to enforce consistent cardiac segmentation and motion estimation over time, without requiring per-frame annotations.
arXiv Computer Vision
Sep 7
Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography
The paper introduces ORBIT, a self‑supervised method for detecting end‑diastolic and end‑systolic cardiac phases in fetal echocardiography without manual annotations. ORBIT learns a latent motion trajectory through registration, enabling orientation‑robust identification of phase transitions across diverse fetal heart positions. Evaluated on normal and congenital heart disease cases, it achieves low mean absolute errors (≈1.9–2.4 frames) and outperforms prior annotation‑free approaches that assume fixed orientations.
arXiv Machine Learning
Sep 10
A Quantitative Evaluation Framework for Temporal Explainability in Echocardiographic Video Segmentation
arXiv:2609.08043v1 Announce Type: cross Abstract: Deep learning has achieved state-of-the-art performance in echocardiographic video segmentation, with an increasing number of models incorporating te...