SOURCE-LINKED INTELLIGENCE
Self-Supervised Cardiac Phase Detection via Single-Parameter Latent Orbits
Accurate identification of end-diastole (ED) and end-systole (ES) in echocardiography underpins the quantification of ventricular function, yet manual selection of these key frames is subjective and introduces clinically significant inter-operator variability. Recent self-supervised methods either prescribe strict periodic trajectories or learn an unconstrained low-dimensional motion subspace from reconstruction or registration objectives. The former offers interpretability but imposes restrictive assumptions on temporal progression, whereas the latter leaves cardiac phase implicit and ED/ES m
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-10T14:56:16.000Z
First collected: 2026-09-20T19:02:05.452Z. This is not the publication date.