CauDR: A Causality-inspired Domain Generalization Framework for
  Fundus-based Diabetic Retinopathy Grading
Hao Wei Peilun Shi Juzheng Miao Minqing Zhang Guitao Bai Jianing Qiu Furui Liu Wu Yuan
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.