临床图介导的非配MRI到CFI高血压预测
Clinical Graph-Mediated Distillation for Unpaired MRI-to-CFI Hypertension Prediction
AI总结：
本文提出CGMD框架，通过构建临床相似性kNN图将MRI中的高血压知识迁移至视网膜模型，提升非配MRI-视网膜数据下的高血压预测性能。
摘要：
视网膜底象限成像可实现低成本可扩展的高血压筛查，但与高血压相关的视网膜线索微妙，导致预测方差高。脑部MRI提供更强的血管和微血管病变标志物，但其昂贵且很少与底象限图像同时获取，导致模态孤立的数据集，MRI和底象限队列不重叠。我们研究这种非配MRI-底象限情形，并引入临床图介导的蒸馏（CGMD）框架，该框架在无需配对多模态数据的情况下将MRI衍生的高血压知识转移至底象限模型。CGMD通过构建跨越两队列的临床相似性kNN图，利用共享的结构化生物标志物作为桥梁。我们训练MRI教师模型，将其表示传播到图中，并为底象限患者推断脑部引导的表示目标。然后通过结合高血压监督、目标蒸馏和关系蒸馏的联合目标训练底象限学生模型。在我们新收集的非配MRI-底象限-生物标志物数据集上的实验表明，CGMD在标准蒸馏和非图插补基线之上一致提升了底象限基于的高血压预测性能，消融实验确认了临床基础图连接的重要性。代码可在https://github.com/DillanImans/CGMD-unpaired-distillation获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
