{"slug": "reveal-differentiable-phenotypic-grouping-for-vision-language-retinal-modeling-s", "title": "REVEAL++: Differentiable Phenotypic Grouping for Vision-Language Retinal Modeling of Alzheimer's Disease Risk", "summary": "Researchers propose REVEAL++, a differentiable phenotypic grouping method for vision-language retinal modeling that improves Alzheimer's disease risk prediction by treating phenotypic similarity as a continuous, learnable signal rather than discrete group assignments. The framework outperforms existing methods on UK Biobank data.", "body_md": "arXiv:2606.19522v1 Announce Type: new\nAbstract: The retina offers a noninvasive window into neurodegenerative disease, capturing subtle structural patterns associated with a risk of future cognitive decline. Vision-language alignment frameworks such as REVEAL have shown that pairing retinal fundus images with structured clinical risk narratives improves early prediction of Alzheimer's disease (AD). A key design choice in these approaches is the use of phenotypic grouping, where individuals with similar risk profiles are treated as multi-positive pairs during contrastive learning. However, existing methods operationalize phenotypic similarity as a discrete construct, relying on hard group assignments that impose rigid supervision and decouple group formation from representation learning. We propose a continuous formulation of phenotypic structure within contrastive learning. Rather than assigning samples to fixed clusters, we model inter-subject similarity as a differentiable weighting function derived from intra-modality embedding similarities in both retinal images and risk profiles. These weights define soft multi-positive relationships through a continuous aggregation operator, enabling graded supervision that reflects the spectrum nature of disease risk. We further introduce a soft-target contrastive objective that jointly learns cross-modal alignment and phenotypic structure in an end-to-end manner. Evaluated on UK Biobank retinal imaging data for incident AD prediction, the proposed framework consistently outperforms discrete group-based contrastive learning and standard vision-language baselines. By treating phenotypic similarity as a learnable, continuous signal rather than a fixed grouping rule, our approach provides a principled and robust foundation for population-scale neurodegenerative risk modeling from multi-modal retinal and clinical data.", "url": "https://wpnews.pro/news/reveal-differentiable-phenotypic-grouping-for-vision-language-retinal-modeling-s", "canonical_source": "https://arxiv.org/abs/2606.19522", "published_at": "2026-06-19 04:00:00+00:00", "updated_at": "2026-06-19 04:03:06.200502+00:00", "lang": "en", "topics": ["machine-learning", "computer-vision", "natural-language-processing", "ai-research", "ai-products"], "entities": ["REVEAL++", "UK Biobank", "Alzheimer's disease"], "alternates": {"html": "https://wpnews.pro/news/reveal-differentiable-phenotypic-grouping-for-vision-language-retinal-modeling-s", "markdown": "https://wpnews.pro/news/reveal-differentiable-phenotypic-grouping-for-vision-language-retinal-modeling-s.md", "text": "https://wpnews.pro/news/reveal-differentiable-phenotypic-grouping-for-vision-language-retinal-modeling-s.txt", "jsonld": "https://wpnews.pro/news/reveal-differentiable-phenotypic-grouping-for-vision-language-retinal-modeling-s.jsonld"}}