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arXiv Computer Vision By John Bonnici, Matthew Baugh, Aleksandra Kulbaka, Sarah Cechnicka, Bernhard Kainz, Alberto Gomez

Self-Supervised Cardiac Phase Detection via Single-Parameter Latent Orbits

Read the original on arXiv Computer Vision →

The paper introduces a self‑supervised method for detecting end‑diastole (ED) and end‑systole (ES) in echocardiography by constraining the latent motion to a single‑parameter orbit, effectively modeling cardiac phase as a one‑dimensional signal. This approach yields an interpretable representation that directly identifies ED and ES, improving ED localisation and matching ES performance compared to prior state‑of‑the‑art methods, while using fewer training epochs and a more constrained model. The method is trained on EchoNet‑Dynamic without annotations and the code is publicly available.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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