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Computer Science > Computer Vision and Pattern Recognition
Title: JointViT: Modeling Oxygen Saturation Levels with Joint Supervision on Long-Tailed OCTA
(Submitted on 17 Apr 2024 (v1), last revised 18 Apr 2024 (this version, v2))
Abstract: 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 this https URL
Submission history
From: Zeyu Zhang [view email][v1] Wed, 17 Apr 2024 16:16:12 GMT (2467kb,D)
[v2] Thu, 18 Apr 2024 08:23:05 GMT (2467kb,D)
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