Clinical Graph-Mediated Distillation for Unpaired MRI-to-CFI Hypertension Prediction

TL;DR

This paper introduces a novel graph-based distillation framework that transfers hypertension prediction knowledge from brain MRI data to fundus image models without requiring paired datasets, improving prediction accuracy.

Contribution

The study presents Clinical Graph-Mediated Distillation (CGMD), a new method leveraging clinical similarity graphs to enable knowledge transfer across unpaired MRI and fundus datasets.

Findings

CGMD improves hypertension prediction accuracy over baselines.

Graph connectivity based on clinical biomarkers is crucial for effective transfer.

The method outperforms standard distillation and non-graph approaches.

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.