{"type":"ScholarlyArticle","title":"OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images","authors":[{"name":"Yang Li","slug":"yang-li","given":"Yang","family":"Li","affiliation":"School of Data Science , Fudan University"},{"name":"Qiuyi Huang","slug":"qiuyi-huang","given":"Qiuyi","family":"Huang","affiliation":"Department of Applied Mathematics , The Hong Kong Polytechnic University"},{"name":"Chong Zhong","slug":"chong-zhong","given":"Chong","family":"Zhong","affiliation":"Department of Applied Mathematics , The Hong Kong Polytechnic University"},{"name":"Danjuan Yang","slug":"danjuan-yang","given":"Danjuan","family":"Yang","affiliation":"Eye Institute and"},{"name":"Meiyan Li","slug":"meiyan-li","given":"Meiyan","family":"Li","affiliation":"Eye Institute and"},{"name":"A.H. Welsh","slug":"a-h-welsh","given":"A.H.","family":"Welsh","affiliation":"College of Business and Economics , Australian National University"},{"name":"Aiyi Liu","slug":"aiyi-liu","given":"Aiyi","family":"Liu","affiliation":"Biostatistics and Bioinformatics Branch , Eunice Kennedy Shriver National Institute of Child Health and Human Development; Department of Ophthalmology , Eye & ENT Hospital , Fudan University"},{"name":"Bo Fu","slug":"bo-fu","given":"Bo","family":"Fu","affiliation":"School of Data Science , Fudan University"},{"name":"Catherine C. Liu","slug":"catherine-c-liu","given":"Catherine C.","family":"Liu","affiliation":"Department of Applied Mathematics , The Hong Kong Polytechnic University"},{"name":"Xingtao Zhou","slug":"xingtao-zhou","given":"Xingtao","family":"Zhou","affiliation":"Eye Institute and"}],"abstract":"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. \r\nImportantly, 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.","doi":"10.24963/ijcai.2024/655","url":"https://ijcai.org/proceedings/2024/655","pdf":"https://ijcai.org/proceedings/2024/0655.pdf","markdown":"https://ijcai.org/proceedings/2024/655.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/655","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":655,"pages":{"first":5927,"last":5935},"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"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal et al., 2017] Kanika Aggarwal, Aniruddha Agar- wal, Ankit Deokar, Ramandeep Singh, Reema Bansal, Aman Sharma, Kusum Sharma, Mangat R Dogra, and Vishali Gupta. 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