  Accurate fovea localization is essential for analyzing retinal diseases to
prevent irreversible vision loss. While current deep learning-based methods
outperform traditional ones, they still face challenges such as the lack of
local anatomical landmarks around the fovea, the inability to robustly handle
diseased retinal images, and the variations in image conditions. In this paper,
we propose a novel transformer-based architecture called DualStreamFoveaNet
(DSFN) for multi-cue fusion. This architecture explicitly incorporates
long-range connections and global features using retina and vessel
distributions for robust fovea localization. We introduce a spatial attention
mechanism in the dual-stream encoder to extract and fuse self-learned
anatomical information, focusing more on features distributed along blood
vessels and significantly reducing computational costs by decreasing token
numbers. Our extensive experiments show that the proposed architecture achieves
state-of-the-art performance on two public datasets and one large-scale private
dataset. Furthermore, we demonstrate that the DSFN is more robust on both
normal and diseased retina images and has better generalization capacity in
cross-dataset experiments.
