TY - GEN
T1 - Prior-SSL
T2 - 32nd International Conference on Artificial Neural Networks, ICANN 2023
AU - Zhang, Huihong
AU - Zhang, Xiaoqing
AU - Zhang, Yinlin
AU - Higashita, Risa
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Choroid structure is crucial for the diagnosis of ocular diseases, and deep supervised learning (SL) techniques have been widely applied to segment the choroidal structure based on OCT images. However, SL requires massive annotated data, which is difficult to obtain. Researchers have explored semi-supervised learning (SSL) methods based on consistency regularization and achieved strong performance. However, these methods suffer from heavy computational burdens and introduce noise that hinders the training process. To address these issues, we propose a thickness distribution prior and uncertainty aware pseudo-label selection SSL framework (Prior-SSL) for OCT choroidal segmentation. Specifically, we compute the instance-level uncertainty of the pseudo-label candidate, which significantly reduces the computational burden of uncertainty estimation. In addition, we consider the physiological characteristics of the choroid, explore the choroidal thickness distribution as prior knowledge in the pseudo-label selection procedure, and thereby obtain more reliable and accurate pseudo-labels. Finally, these two branches are combined via a Modified AND-Gate (MAG) to assign confidence levels to pseudo-label candidates. We achieve state-of-the-art performance for the choroidal segmentation task on the GOALS and NIDEK OCT datasets. Ablation studies verify the effectiveness of the Prior-SSL in selecting high-quality pseudo-labels.
AB - Choroid structure is crucial for the diagnosis of ocular diseases, and deep supervised learning (SL) techniques have been widely applied to segment the choroidal structure based on OCT images. However, SL requires massive annotated data, which is difficult to obtain. Researchers have explored semi-supervised learning (SSL) methods based on consistency regularization and achieved strong performance. However, these methods suffer from heavy computational burdens and introduce noise that hinders the training process. To address these issues, we propose a thickness distribution prior and uncertainty aware pseudo-label selection SSL framework (Prior-SSL) for OCT choroidal segmentation. Specifically, we compute the instance-level uncertainty of the pseudo-label candidate, which significantly reduces the computational burden of uncertainty estimation. In addition, we consider the physiological characteristics of the choroid, explore the choroidal thickness distribution as prior knowledge in the pseudo-label selection procedure, and thereby obtain more reliable and accurate pseudo-labels. Finally, these two branches are combined via a Modified AND-Gate (MAG) to assign confidence levels to pseudo-label candidates. We achieve state-of-the-art performance for the choroidal segmentation task on the GOALS and NIDEK OCT datasets. Ablation studies verify the effectiveness of the Prior-SSL in selecting high-quality pseudo-labels.
KW - OCT image
KW - choroidal segmentation
KW - prior knowledge
KW - pseudo-label
KW - semi-supervised learning (SSL)
UR - http://www.scopus.com/inward/record.url?scp=85174607601&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-44210-0_46
DO - 10.1007/978-3-031-44210-0_46
M3 - Conference contribution
AN - SCOPUS:85174607601
SN - 9783031442094
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 570
EP - 581
BT - Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings
A2 - Iliadis, Lazaros
A2 - Papaleonidas, Antonios
A2 - Angelov, Plamen
A2 - Jayne, Chrisina
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 September 2023 through 29 September 2023
ER -