BILATERAL-VIT FOR ROBUST FOVEA LOCALIZATION

Song, Sifan ORCID: 0000-0002-7940-650X, Dang, Kang, Yu, Qinji, Wang, Zilong, Coenen, Frans ORCID: 0000-0003-1026-6649, Su, Jionglong and Ding, Xiaowei (2022) BILATERAL-VIT FOR ROBUST FOVEA LOCALIZATION. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022-3-28 - 2022-3-31.

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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 using the Messidor and PALM datasets.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Fovea Localization, Vision Transformer, Bilateral Neural Network, Feature Fusion
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	21 Jun 2022 08:39
Last Modified:	14 Mar 2024 21:44
DOI:	10.1109/ISBI52829.2022.9761523
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3156876