跨模态迭代蒸馏用于稳健的IHD筛查：IDNet框架与一个新基准
Cross-Modal Iteration Distillation for Robust IHD Screening: The IDNet Framework and A New Benchmark
AI总结：
提出IDNet框架，通过跨模态蒸馏聚合器(CDA)融合左右眼眼底图像和稀疏临床变量，并构建UK Biobank基准，在IHD筛查中优于多种基线方法。
摘要：
彩色眼底摄影(CFP)为缺血性心脏病(IHD)筛查提供了一条低成本、非侵入性的途径，但当前研究受限于稀缺的公开基准以及视网膜图像与稀疏临床变量的无效融合。我们提出了IDNet，一个多模态框架，其中包含一个跨模态蒸馏聚合器(CDA)，该聚合器使用可学习查询依次整合左眼、右眼和临床特征，缓解了高维视觉特征与低维表格输入之间的不平衡。我们还通过开源整理和质量控制流程构建了一个可复现的UK Biobank基准，包含来自25,205名受试者的50,410张图像。在该基准上，IDNet优于纯图像、纯临床以及多种多模态基线，并且CDA作为即插即用的融合模块持续提升了多种视觉编码器的性能。
英文摘要：
Color Fundus Photography (CFP) offers a low-cost and non-invasive route for ischemic heart disease (IHD) screening, but current studies are limited by scarce public benchmarks and ineffective fusion of retinal images with sparse clinical variables. We propose IDNet, a multimodal framework with a Cross-Modal Distillation Aggregator (CDA) that uses learnable queries to sequentially integrate left-eye, right-eye, and clinical features, mitigating the imbalance between high-dimensional visual features and low-dimensional tabular inputs. We also construct a reproducible UK Biobank benchmark with open-source curation and quality-control pipelines, yielding 50,410 images from 25,205 subjects. On this benchmark, IDNet outperforms image-only, clinical-only, and several multimodal baselines, and CDA consistently improves multiple visual encoders as a plug-in fusion module.
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。
