Bilateral-Fuser: A Novel Multi-cue Fusion Architecture with Anatomical-aware Tokens for Fovea Localization

Accurate localization of the fovea is a crucial initial step in analyzing retinal diseases since it helps prevent irreversible vision loss. Although current deep learning-based methods achieve better performance than traditional methods, they still face challenges such as inadequate utilization of anatomical landmarks, sensitivity to diseased retinal images, and various image conditions. In this paper, we propose a novel transformer-based architecture (Bilateral-Fuser) for multi-cue fusion. The Bilateral-Fuser explicitly incorporates long-range connections and global features using retina and vessel distributions to achieve robust fovea localization. We introduce a spatial attention mechanism in the dual-stream encoder to extract and fuse self-learned anatomical information. This design focuses more on features distributed along blood vessels and significantly reduces computational costs by reducing token numbers. Our comprehensive experiments demonstrate that the proposed architecture achieves state-of-the-art performance on two public datasets and one large-scale private dataset. Moreover, we show that the Bilateral-Fuser is more robust on both normal and diseased retina images and has better generalization capacity in cross-dataset experiments.