JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909271542726656 |
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| author | 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 |
| author_facet | 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 |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_11525 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA 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 Computer Vision and Pattern Recognition Image and Video Processing 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 |
| title | JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2404.11525 |