TY - GEN
T1 - JointViT
T2 - 28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024
AU - Zhang, Zeyu
AU - Qi, Xuyin
AU - Chen, Mingxi
AU - Li, Guangxi
AU - Pham, Ryan
AU - Qassim, Ayub
AU - Berry, Ella
AU - Liao, Zhibin
AU - Siggs, Owen
AU - Mclaughlin, Robert
AU - Craig, Jamie
AU - To, Minh-Son
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Long-Tailed Distribution
KW - Optical Coherence Tomography Angiography
KW - Oxygen Saturation Levels
UR - http://www.scopus.com/inward/record.url?scp=85200682455&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-66955-2_11
DO - 10.1007/978-3-031-66955-2_11
M3 - Conference contribution
AN - SCOPUS:85200682455
SN - 9783031669545
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 158
EP - 172
BT - Medical Image Understanding and Analysis -
A2 - Yap, Moi Hoon
A2 - Kendrick, Connah
A2 - Behera, Ardhendu
A2 - Cootes, Timothy
A2 - Zwiggelaar, Reyer
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham, Switzerland
Y2 - 24 July 2024 through 26 July 2024
ER -