Diabetic retinopathy (DR) is the most common diabetic complication, which
usually leads to retinal damage, vision loss, and even blindness. A
computer-aided DR grading system has a significant impact on helping
ophthalmologists with rapid screening and diagnosis. Recent advances in fundus
photography have precipitated the development of novel retinal imaging cameras
and their subsequent implementation in clinical practice. However, most deep
learning-based algorithms for DR grading demonstrate limited generalization
across domains. This inferior performance stems from variance in imaging
protocols and devices inducing domain shifts. We posit that declining model
performance between domains arises from learning spurious correlations in the
data. Incorporating do-operations from causality analysis into model
architectures may mitigate this issue and improve generalizability.
Specifically, a novel universal structural causal model (SCM) was proposed to
analyze spurious correlations in fundus imaging. Building on this, a
causality-inspired diabetic retinopathy grading framework named CauDR was
developed to eliminate spurious correlations and achieve more generalizable DR
diagnostics. Furthermore, existing datasets were reorganized into 4DR benchmark
for DG scenario. Results demonstrate the effectiveness and the state-of-the-art
(SOTA) performance of CauDR.

通过引入因果分析中的双操作到模型架构中，本文提出了一种新颖的通用结构因果模型（SCM），用于分析视网膜成像中的假相关性，并在此基础上发展了一种名为 CauDR 的因果关联性糖尿病视网膜病变分级框架，以消除假相关性并实现更具普适性的糖尿病视网膜病变诊断。结果表明 CauDR 具有高效性和最新技术水平的表现。