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.

(06/18/2024) 🎉 Our paper has been selected as oral presentation at MIUA 2024!

(05/14/2024) 🎉 Our paper has been accepted to MIUA 2024!

@article{zhang2024jointvit,
  title={JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA},
  author={Zhang, Zeyu and Qi, Xuyin and Chen, Mingxi and Li, Guangxi and Pham, Ryan and Zuhair, Ayub and Berry, Ella and Liao, Zhibin and Siggs, Owen and Mclaughlin, Robert and others},
  journal={arXiv preprint arXiv:2404.11525},
  year={2024}
}