  Glaucoma is a leading cause of irreversible blindness worldwide. While deep
learning approaches using fundus images have largely improved early diagnosis
of glaucoma, variations in images from different devices and locations (known
as domain shifts) challenge the use of pre-trained models in real-world
settings. To address this, we propose a novel Graph-guided Test-Time Adaptation
(GTTA) framework to generalize glaucoma diagnosis models to unseen test
environments. GTTA integrates the topological information of fundus images into
the model training, enhancing the model's transferability and reducing the risk
of learning spurious correlation. During inference, GTTA introduces a novel
test-time training objective to make the source-trained classifier
progressively adapt to target patterns with reliable class conditional
estimation and consistency regularization. Experiments on cross-domain glaucoma
diagnosis benchmarks demonstrate the superiority of the overall framework and
individual components under different backbone networks.
