# Copula-enhanced Vision Transformer for high myopia diagnosis through OU UWF fundus images

arXiv: https://arxiv.org/abs/2501.06540 · HTML: https://mlsanity.com/paper/2501.06540/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 11 Jan 2025.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Scaling Vision Transformers](https://mlsanity.com/paper/2106.04560/) (2021)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [A Modulation Module for Multi-task Learning with Applications in Image Retrieval](https://mlsanity.com/paper/1807.06708/) (2018)
- [Benign Overfitting and Noisy Features](https://mlsanity.com/paper/2008.02901/) (2020)
- [Differentiating the multipoint Expected Improvement for optimal batch design](https://mlsanity.com/paper/1503.05509/) (2015)
- [Bayesian Data Synthesis and the Utility-Risk Trade-Off for Mixed Epidemiological Data](https://mlsanity.com/paper/2102.08255/) (2021)
- [A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values](https://mlsanity.com/paper/1806.04610/) (2018)
- [CeCNN: Copula-enhanced convolutional neural networks in joint prediction of refraction error and axial length based on ultra-widefield fundus images](https://mlsanity.com/paper/2311.03967/) (2023)
- [OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images](https://mlsanity.com/paper/2403.11974/) (2024)
- [An Expectation Conditional Maximization approach for Gaussian graphical models](https://mlsanity.com/paper/1709.06970/) (2017)
- [Bayesian Variable Selection for Gaussian copula regression models](https://mlsanity.com/paper/1907.08245/) (2019)

## Abstract (arXiv)

The advancement of AI-assisted myopia screening necessitates the joint diagnosis of both-eye (OU) high myopia (HM) status and the prediction of axial length (AL). This clinical requirement introduces a complex mixed-type (binary-continuous) multitask learning task with bi-domain (OU) image covariates, giving rise to two key challenges: i) capture the inter-ocular asymmetry of OU images within a cutting-edge foundation model; ii) model and estimate the conditional dependence structure among mixed-type multivariate responses given image covariates. We address the challenges by: i) imposing residual adapters on the Vision Transformer foundation model to capture the OU similarity and heterogeneity simultaneously; ii) developing a four-dimensional copula loss that is implementable in PyTorch based on a latent variable expression for the Gaussian copula likelihood, and proposing a computationally efficient fast Monte Carlo Expectation Maximization (fMCEM) algorithm to estimate copula parameters. We further formulate a specific overfitting problem called stronger covariance phenomenon in multitask learning. We reveal the disturbance of the phenomenon to estimation of copula parameters and theoretically demonstrate the numerical stability of the proposed fMCEM algorithm against the disturbance. The application to our annotated OU ultra-widefield fundus image dataset and simulation on synthetic data demonstrate that our method stably enhances the predictive capabilities on both classification and regression tasks.