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ARXIV:2603.21809 · MEDICAL AI · SUBMITTED 24 MAR · 21:26 UTC · FRESHNESS STALE
ARXIV:2603.21809MEDICAL AISUBMITTED 24 MAR · 21:26 UTCFRESHNESS STALEDillan Imans · Phuoc-Nguyen Bui · Duc-Tai Le · Hyunseung Choo · arXiv
A framework that transfers hypertension prediction knowledge from expensive MRI scans to low-cost retinal fundus images using a clinical similarity graph, enabling better screening with unpaired data.
Opportunity summary
Pain A framework that transfers hypertension prediction knowledge from expensive MRI scans to low-cost retinal fundus images using a clinical similarity graph, enabling better screening with unpaired data.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence unverified
A framework that transfers hypertension prediction knowledge from expensive MRI scans to low-cost retinal fundus images using a clinical similarity graph, enabling better screening with unpaired data. Brain MRI provides stronger vascular and small-vessel-disease…
Retinal fundus imaging enables low-cost and scalable hypertension (HTN) screening, but HTN-related retinal cues are subtle, yielding high-variance predictions. Brain MRI provides stronger vascular and small-vessel-disease markers of HTN, yet it is expensive and…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Retinal fundus imaging enables low-cost and scalable hypertension (HTN) screening, but HTN-related retinal cues are subtle, yielding high-variance predictions. A public repository is linked,…
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A framework that transfers hypertension prediction knowledge from expensive MRI scans to low-cost retinal fundus images using a clinical similarity graph, enabling better screening with unpaired data.
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10.48550/arXiv.2603.21809A framework that transfers hypertension prediction knowledge from expensive MRI scans to low-cost retinal fundus images using a clinical similarity graph, enabling better screening with unpaired data.
Abstract
Retinal fundus imaging enables low-cost and scalable hypertension (HTN) screening, but HTN-related retinal cues are subtle, yielding high-variance predictions. Brain MRI provides stronger vascular and small-vessel-disease markers of HTN, yet it is expensive and rarely acquired alongside fundus images, resulting in modality-siloed datasets with disjoint MRI and fundus cohorts. We study this unpaired MRI-fundus regime and introduce Clinical Graph-Mediated Distillation (CGMD), a framework that transfers MRI-derived HTN knowledge to a fundus model without paired multimodal data. CGMD leverages shared structured biomarkers as a bridge by constructing a clinical similarity kNN graph spanning both cohorts. We train an MRI teacher, propagate its representations over the graph, and impute brain-informed representation targets for fundus patients. A fundus student is then trained with a joint objective combining HTN supervision, target distillation, and relational distillation. Experiments on our newly collected unpaired MRI-fundus-biomarker dataset show that CGMD consistently improves fundus-based HTN prediction over standard distillation and non-graph imputation baselines, with ablations confirming the importance of clinically grounded graph connectivity. Code is available at https://github.com/DillanImans/CGMD-unpaired-distillation.
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PROBLEM
A framework that transfers hypertension prediction knowledge from expensive MRI scans to low-cost retinal fundus images using a clinical similarity graph, enabling better screening with unpaired data. Brain MRI provides stronger vascular and small-vessel-disease markers of HTN,...
METHOD
Retinal fundus imaging enables low-cost and scalable hypertension (HTN) screening, but HTN-related retinal cues are subtle, yielding high-variance predictions. Brain MRI provides stronger vascular and small-vessel-disease markers of HTN, yet it is expensive and rarely acquired a...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Retinal fundus imaging enables low-cost and scalable hypertension (HTN) screening, but HTN-related retinal cues are subtle, yielding high-variance predictions. A public repository is linked, so build veri...
WHY NOW
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A framework that transfers hypertension prediction knowledge from expensive MRI scans to low-cost retinal fundus images using a clinical similarity graph, enabling better screening with unpaired data. Brain MRI provides stronger vascular and small-vessel-disease markers of HTN, yet it is expensive and rarely acquired alongside fundus images, resulting in modality-siloed datasets with disjoint MRI and fundus cohorts.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Retinal fundus imaging enables low-cost and scalable hypertension (HTN) screening, but HTN-related retinal cues are subtle, yielding high-variance predictions. Brain MRI provides stronger vascular and small-vessel-disease markers of HTN, yet it is expensive and rarely acquired alongside fundus images, resulting in modality-siloed datasets with disjoint MRI and fundus cohorts.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Retinal fundus imaging enables low-cost and scalable hypertension (HTN) screening, but HTN-related retinal cues are subtle, yielding high-variance predictions. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Medical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework that transfers hypertension prediction knowledge from expensive MRI scans to low-cost retinal fundus images using a clinical similarity graph, enabling better screening with unpaired data.
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