Report
JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA
| العنوان: | JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA |
|---|---|
| المؤلفون: | 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 |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing |
| الوصف: | 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 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.11525 |
| رقم الانضمام: | edsarx.2404.11525 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |