Bilateral-ViT for Robust Fovea Localization
- URL: http://arxiv.org/abs/2110.09860v1
- Date: Tue, 19 Oct 2021 11:26:04 GMT
- Title: Bilateral-ViT for Robust Fovea Localization
- Authors: Sifan Song, Kang Dang, Qinji Yu, Zilong Wang, Frans Coenen, Jionglong
Su, Xiaowei Ding
- Abstract summary: This paper proposes a novel vision transformer (ViT) approach that integrates information both inside and outside the fovea region.
Our comprehensive experiments demonstrate that the proposed approach is significantly more robust for diseased images.
- Score: 6.754429047600573
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The fovea is an important anatomical landmark of the retina. Detecting the
location of the fovea is essential for the analysis of many retinal diseases.
However, robust fovea localization remains a challenging problem, as the fovea
region often appears fuzzy, and retina diseases may further obscure its
appearance. This paper proposes a novel vision transformer (ViT) approach that
integrates information both inside and outside the fovea region to achieve
robust fovea localization. Our proposed network named
Bilateral-Vision-Transformer (Bilateral-ViT) consists of two network branches:
a transformer-based main network branch for integrating global context across
the entire fundus image and a vessel branch for explicitly incorporating the
structure of blood vessels. The encoded features from both network branches are
subsequently merged with a customized multi-scale feature fusion (MFF) module.
Our comprehensive experiments demonstrate that the proposed approach is
significantly more robust for diseased images and establishes the new state of
the arts on both Messidor and PALM datasets.
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