  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 that 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.
