OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images
OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images
Yang Li, Qiuyi Huang, Chong Zhong, Danjuan Yang, Meiyan Li, A.H. Welsh, Aiyi Liu, Bo Fu, Catherine C. Liu, Xingtao Zhou
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 5927-5935.
https://doi.org/10.24963/ijcai.2024/655
Myopia screening using cutting-edge ultra-widefield (UWF) fundus imaging is potentially significant for ophthalmic outcomes. Current multidisciplinary research between ophthalmology and deep learning (DL) concentrates primarily on disease classification and diagnosis using single-eye images, largely ignoring joint modeling and prediction for Oculus Uterque (OU, both eyes). Inspired by the complex relationships between OU and the high correlation between the (continuous) outcome labels (Spherical Equivalent and Axial Length), we propose a framework of copula-enhanced adapter convolutional neural network (CNN) learning with OU UWF fundus images (OUCopula) for joint prediction of multiple clinical scores. We design a novel bi-channel multi-label CNN which can (1) take bi-channel image inputs subject to both high correlation and heterogeneity (by sharing the same backbone network and employing adapters to parameterize the channel-wise discrepancy), and (2) incorporate correlation information between continuous output labels (using a copula). Solid experiments show that OUCopula achieves satisfactory performance in myopia score prediction compared to backbone models. Moreover, OUCopula can far exceed the performance of models constructed for single-eye inputs.
Importantly, our study also hints at the potential extension of the bi-channel model to a multi-channel paradigm and the generalizability of OUCopula across various backbone CNNs. The code and the supplementary materials are available at: github.com/Charley-HUANG/OUCopula.
Keywords:
Multidisciplinary Topics and Applications: MTA: Bioinformatics
Computer Vision: CV: Biomedical image analysis
Machine Learning: ML: Multi-label learning
Multidisciplinary Topics and Applications: MTA: Health and medicine