| Title: |
JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA |
| Authors: |
Zhang, Zeyu, Qi, Xuyin, Chen, Mingxi, Li, Guangxi, Pham, Ryan, Qassim, Ayub, Berry, Ella, Liao, Zhibin, Siggs, Owen, Mclaughlin, Robert, Craig, Jamie, To, Minh-Son |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing |
| More Details: |
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
Comment: Accepted to MIUA 2024 Oral |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2404.11525 |
| Accession Number: |
edsarx.2404.11525 |
| Database: |
arXiv |
