{"slug": "hypoproto-hyperbolic-ordinal-prototypes-for-left-ventricular-filling-pressure", "title": "HypOProto: Hyperbolic Ordinal Prototypes for Left Ventricular Filling Pressure Classification", "summary": "Researchers propose HypOProto, a hyperbolic prototype-based framework for interpretable left ventricular filling pressure classification from B-mode echocardiography, achieving state-of-the-art performance while maintaining clinical transparency. The method arranges prototypes along the physiological E/e' scale and introduces a novel Hyperbolic Prototype Angular Separation loss to enforce inter-class separation.", "body_md": "arXiv:2606.19804v1 Announce Type: new\nAbstract: Echocardiography (echo) is a widely used imaging modality for assessing cardiac function, with Left Ventricular Filling Pressure (LVFP) serving as a critical physiological marker for conditions such as heart failure. Standard LVFP classification into normal \\emph{vs} elevated categories relies on the Doppler-derived $E/e'$ ratio, which is operator-dependent and often unavailable in resource-limited settings, motivating methods that infer LVFP directly from B-mode echo. Existing deep learning approaches achieve high performance but remain largely black-box, limiting clinical interpretability. We propose HypOProto, a hyperbolic, ordinal prototype-based framework for interpretable LVFP classification using a frozen, explainable foundation model backbone. HypOProto arranges prototypes along the physiological $E/e'$ scale, placing borderline cases near the hyperboloid root where small angular differences separate similar cases, while normal and elevated cases occupy outward positions reflecting increasing diagnostic certainty. This hyperbolic geometry encodes clinically meaningful ordinal relationships and improves interpretability. We also introduce a novel Hyperbolic Prototype Angular Separation (HyperPAS) loss, enforcing inter-class prototype separation in hyperbolic space. HypOProto achieves SOTA performance while maintaining transparency, and highlights clinically relevant regions in visualizations. This work represents the first prototype-based framework for LVFP classification in echo. Our code can be found at https://github.com/DeepRCL/HypOProto.", "url": "https://wpnews.pro/news/hypoproto-hyperbolic-ordinal-prototypes-for-left-ventricular-filling-pressure", "canonical_source": "https://arxiv.org/abs/2606.19804", "published_at": "2026-06-19 04:00:00+00:00", "updated_at": "2026-06-19 04:01:48.012449+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision", "ai-research"], "entities": ["HypOProto", "DeepRCL"], "alternates": {"html": "https://wpnews.pro/news/hypoproto-hyperbolic-ordinal-prototypes-for-left-ventricular-filling-pressure", "markdown": "https://wpnews.pro/news/hypoproto-hyperbolic-ordinal-prototypes-for-left-ventricular-filling-pressure.md", "text": "https://wpnews.pro/news/hypoproto-hyperbolic-ordinal-prototypes-for-left-ventricular-filling-pressure.txt", "jsonld": "https://wpnews.pro/news/hypoproto-hyperbolic-ordinal-prototypes-for-left-ventricular-filling-pressure.jsonld"}}