Li, YangHuang, QiuyiZhong, ChongYang, DanjuanLi, MeiyanWelsh, A. H.Liu, AiyiFu, BoLiu, Catherine C.Zhou, Xingtao2025-05-232025-05-2397819567920411045-0823http://www.scopus.com/inward/record.url?scp=85202049493&partnerID=8YFLogxKhttps://hdl.handle.net/1885/733752147Myopia 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.YL and BF's research are supported by National Natural Science Foundation of China (71991471, 12071089). QH is supported by General Research Fund 15301123, RGC, HKSAR. CZ is supported by the Postdoc Fellowship of CAS AMSS-PolyU Joint Laboratory of Applied Mathematics and PolyU Research Grant P0045497. Research of DY and ML are supported by the Shanghai Rising-Star Program (21QA1401500). ML's research is also supported by National Natural Science Foundation of China (82371091). Research of AW is supported by the Australian Research Council Discovery Project (DP230101908). Research of AL is supported by the Intramural Research Program of the National Institute of Child Health and Human Development. CL's research is supported by General Research Fund 15327216 and 15301123, RGC, HKSAR and National Natural Science Foundation of China (12271060).9enPublisher Copyright: © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.202485202049493