The oxygen saturation level in the blood (SaO2) is crucial for health, particularly in relation to sleep-related breathing disorders. However, continuous monitoring of SaO2 is time-consuming and highly variable depending on patients' conditions. Recently, optical coherence tomography angiography (OCTA) has shown promising development in rapidly and effectively screening eye-related lesions, offering the potential for diagnosing sleep-related disorders. To bridge this gap, our paper presents three key contributions. Firstly, we propose JointViT, a novel model based on the Vision Transformer architecture, incorporating a joint loss function for supervision. Secondly, we introduce a balancing augmentation technique during data preprocessing to improve the model's performance, particularly on the long-tail distribution within the OCTA dataset. Lastly, through comprehensive experiments on the OCTA dataset, our proposed method significantly outperforms other state-of-the-art methods, achieving improvements of up to 12.28% in overall accuracy. This advancement lays the groundwork for the future utilization of OCTA in diagnosing sleep-related disorders. See project website https://steve-zeyu-zhang.github.io/JointViT
@article{arxiv.2404.11525,
title = {JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA},
author = {Zeyu Zhang and Xuyin Qi and Mingxi Chen and Guangxi Li and Ryan Pham and Ayub Qassim and Ella Berry and Zhibin Liao and Owen Siggs and Robert Mclaughlin and Jamie Craig and Minh-Son To},
journal= {arXiv preprint arXiv:2404.11525},
year = {2024}
}