OU-CoViT: Copula-Enhanced Bi-Channel Multi-Task Vision Transformers with Dual Adaptation for OU-UWF Images

Myopia screening using cutting-edge ultra-widefield (UWF) fundus imaging and joint modeling of multiple discrete and continuous clinical scores presents a promising new paradigm for multi-task problems in Ophthalmology. The bi-channel framework that arises from the Ophthalmic phenomenon of ``interocular asymmetries'' of both eyes (OU) calls for new employment on the SOTA transformer-based models. However, the application of copula models for multiple mixed discrete-continuous labels on deep learning (DL) is challenging. Moreover, the application of advanced large transformer-based models to small medical datasets is challenging due to overfitting and computational resource constraints. To resolve these challenges, we propose OU-CoViT: a novel Copula-Enhanced Bi-Channel Multi-Task Vision Transformers with Dual Adaptation for OU-UWF images, which can i) incorporate conditional correlation information across multiple discrete and continuous labels within a deep learning framework (by deriving the closed form of a novel Copula Loss); ii) take OU inputs subject to both high correlation and interocular asymmetries using a bi-channel model with dual adaptation; and iii) enable the adaptation of large vision transformer (ViT) models to small medical datasets. Solid experiments demonstrate that OU-CoViT significantly improves prediction performance compared to single-channel baseline models with empirical loss. Furthermore, the novel architecture of OU-CoViT allows generalizability and extensions of our dual adaptation and Copula Loss to various ViT variants and large DL models on small medical datasets. Our approach opens up new possibilities for joint modeling of heterogeneous multi-channel input and mixed discrete-continuous clinical scores in medical practices and has the potential to advance AI-assisted clinical decision-making in various medical domains beyond Ophthalmology.

CeCNN: Copula-enhanced convolutional neural networks in joint prediction of refraction error and axial length based on ultra-widefield fundus images

Ultra-widefield (UWF) fundus images are replacing traditional fundus images in screening, detection, prediction, and treatment of complications related to myopia because their much broader visual range is advantageous for highly myopic eyes. Spherical equivalent (SE) is extensively used as the main myopia outcome measure, and axial length (AL) has drawn increasing interest as an important ocular component for assessing myopia. Cutting-edge studies show that SE and AL are strongly correlated. Using the joint information from SE and AL is potentially better than using either separately. In the deep learning community, though there is research on multiple-response tasks with a 3D image biomarker, dependence among responses is only sporadically taken into consideration. Inspired by the spirit that information extracted from the data by statistical methods can improve the prediction accuracy of deep learning models, we formulate a class of multivariate response regression models with a higher-order tensor biomarker, for the bivariate tasks of regression-classification and regression-regression. Specifically, we propose a copula-enhanced convolutional neural network (CeCNN) framework that incorporates the dependence between responses through a Gaussian copula (with parameters estimated from a warm-up CNN) and uses the induced copula-likelihood loss with the backbone CNNs. We establish the statistical framework and algorithms for the aforementioned two bivariate tasks. We show that the CeCNN has better prediction accuracy after adding the dependency information to the backbone models. The modeling and the proposed CeCNN algorithm are applicable beyond the UWF scenario and can be effective with other backbones beyond ResNet and LeNet.