基于环形视网膜血管特征的可解释眼底图像分类
Interpretable Fundus Image Classification via Ring-Based Retinal Vasculature Features
- University of Toronto（多伦多大学）
- Duke Kunshan University（昆山杜克大学）
- York University（约克大学）
- Fields Institute for Research in Mathematical Sciences（菲尔兹数学科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出基于环形视网膜血管特征的可解释眼底图像分类框架，仅用血管描述符在HRF数据集达91.1%准确率，与RETFound相当，可支持可解释疾病分类等且无需大量任务训练数据。
AI中文摘要：
视网膜眼底摄影广泛用于眼部疾病的筛查与监测，但许多现代分类流程依赖深度隐层表示，可解释性有限。本研究开发了一种基于以视盘为中心的视网膜血管环形结构表示的可解释眼底图像分类框架，该方法在同心视网膜区域内量化血管几何形状、颜色外观、与氧合相关的血管外观及血管-背景熵；这些生理驱动的描述符源自血管掩码、图像强度和光密度测量值，并跨环聚合以捕捉血管特性的空间变化。仅使用定量血管描述符，所提方法在三个公开眼底数据集上实现了较强的分类性能：在HRF数据集上，使用自动生成的血管掩码达到91.1%的准确率，与在相同评估设置下使用大规模视网膜眼底图像数据预训练的视觉Transformer模型RETFound表现相当。进一步分析表明，预训练图像模型对采集相关的空间线索（包括眼底尺度、视场范围内的视网膜位置）及更广泛的非血管图像特征敏感。该框架无需大量特定任务训练数据集，即可支持可解释的疾病分类、定量视网膜表型分析及视网膜生物标志物发现。
英文摘要：
Retinal fundus photography is widely used for screening and monitoring ocular diseases, but many modern classification pipelines rely on deep latent representations and provide limited interpretability. This study develops an interpretable fundus image classification framework based on a ring-structured representation of the retinal vasculature centered on the optic disc. The method quantifies vessel geometry, color appearance, oxygenation-related vascular appearance, and vessel--background entropy within concentric retinal regions. These physiologically motivated descriptors are derived from vessel masks, image intensities, and optical-density measurements and aggregated across rings to capture spatial variation in vascular properties. Using only quantitative vascular descriptors, the proposed method achieved strong classification performance across three public fundus datasets. On HRF, it achieved 91.1\% accuracy using automatically generated vessel masks, matching RETFound, a vision transformer pretrained on large-scale retinal fundus image data, under the same evaluation setting. Additional analyses suggest that pretrained image models are sensitive to acquisition-related spatial cues, including fundus scale and retinal position within the field of view, as well as broader non-vessel image characteristics. This framework may support interpretable disease classification, quantitative retinal phenotyping, and retinal biomarker discovery without requiring large task-specific training datasets.
