{"slug": "capturing-cardiac-cyclicity-through-phase-equivariant-self-supervised-learning", "title": "Capturing Cardiac Cyclicity through Phase-Equivariant Self-Supervised Learning", "summary": "Researchers introduced Winder, a joint-embedding architecture that uses a phase-equivariant self-supervised objective to organize cardiac representations into phase-invariant and phase-rotating subspaces, achieving diagnostic accuracy comparable to state-of-the-art methods on the PTB-XL dataset with a ~1 million parameter footprint. The transport operator is fixed and closed-form, derived from the cardiac cycle's geometry, adding no parameters. This demonstrates that encoding cardiac-phase symmetry preserves diagnostically useful information while yielding a legible, parameter-efficient latent geometry tied to a measurable physiological quantity.", "body_md": "arXiv:2608.21147v1 Announce Type: new\nAbstract: The cyclic structure of physiological processes offers a natural prior for self-supervised representation learning, and the cardiac cycle provides a particularly well-defined setting in which to exploit it. We derive a phase-equivariant self-supervised objective and introduce Winder, a joint-embedding architecture that organises representations into phase-invariant coordinates and phase-rotating harmonic subspaces. Its transport operator is fixed and closed-form, derived from the cycle's geometry rather than learned, and adds no parameters. Evaluated on PTB-XL under a frozen linear-probe protocol, Winder attains diagnostic accuracy within the range reported by state-of-the-art self-supervised methods at a ~1 M parameter footprint, while exhibiting phase-equivariant latent geometry. These findings demonstrate that explicitly encoding cardiac-phase symmetry can preserve diagnostically useful information while yielding a latent geometry that is legible, parameter-efficient, and directly tied to a measurable physiological quantity.", "url": "https://wpnews.pro/news/capturing-cardiac-cyclicity-through-phase-equivariant-self-supervised-learning", "canonical_source": "https://www.machinebrief.com/news/capturing-cardiac-cyclicity-through-phase-equivariant-self-s-pi6s", "published_at": "2026-08-24 04:00:00+00:00", "updated_at": "2026-08-24 05:14:20.684707+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["Winder", "PTB-XL"], "alternates": {"html": "https://wpnews.pro/news/capturing-cardiac-cyclicity-through-phase-equivariant-self-supervised-learning", "markdown": "https://wpnews.pro/news/capturing-cardiac-cyclicity-through-phase-equivariant-self-supervised-learning.md", "text": "https://wpnews.pro/news/capturing-cardiac-cyclicity-through-phase-equivariant-self-supervised-learning.txt", "jsonld": "https://wpnews.pro/news/capturing-cardiac-cyclicity-through-phase-equivariant-self-supervised-learning.jsonld"}}