# Ordinal Label-Distribution Learning with Constrained Asymmetric Priors for Imbalanced Retinal Grading

arXiv: https://arxiv.org/abs/2509.26146 · HTML: https://mlsanity.com/paper/2509.26146/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Wasserstein Auto-Encoders](https://mlsanity.com/paper/1711.01558/) (2017)
- [Rank consistent ordinal regression for neural networks with application to age estimation](https://mlsanity.com/paper/1901.07884/) (2019)
- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification](https://mlsanity.com/paper/2306.13856/) (2023)
- [Guided Context Gating: Learning to leverage salient lesions in retinal fundus images](https://mlsanity.com/paper/2406.13126/) (2024)
- [On Variants of Root Normalised Order-aware Divergence and a Divergence based on Kendall's Tau](https://mlsanity.com/paper/2204.07304/) (2022)

## Built on by (most cited first, 4 of 4)

- [Directed Ordinal Diffusion Regularization for Progression-Aware Diabetic Retinopathy Grading](https://mlsanity.com/paper/2602.21942/) (2026)
- [D3O: Dynamic Distribution Distillation for Ordinal Regression](https://mlsanity.com/paper/2607.23575/) (2026)
- [Disentangling Co-Occurring Retinal Pathologies with Saliency-Guided Sparse Expert Routing](https://mlsanity.com/paper/2608.09752/) (2026)
- [C2A: Coupling Spatial Evidence with Clinical Priors via Co-occurrence Aware Class Attention for Multi-Label Chest X-Ray Classification](https://mlsanity.com/paper/2608.09774/) (2026)

## Abstract (arXiv)

Diabetic retinopathy grading is inherently ordinal and long-tailed, with minority stages being scarce, heterogeneous, and clinically critical to detect accurately. Conventional methods often rely on isotropic Gaussian priors and symmetric loss functions, misaligning latent representations with the task's asymmetric nature. We propose the Constrained Asymmetric Prior Wasserstein Autoencoder (CAP-WAE), a novel framework that addresses these challenges through three key innovations. Our approach employs a Wasserstein Autoencoder (WAE) that aligns its aggregate posterior with a asymmetric prior, preserving the heavy-tailed and skewed structure of minority classes. The latent space is further structured by a Margin-Aware Orthogonality and Compactness (MAOC) loss to ensure grade-ordered separability. At the supervision level, we introduce a direction-aware ordinal loss, where a lightweight head predicts asymmetric dispersions to generate soft labels that reflect clinical priorities by penalizing under-grading more severely. Stabilized by an adaptive multi-task weighting scheme, our end-to-end model requires minimal tuning. Across public DR benchmarks, CAP-WAE consistently achieves state-of-the-art Quadratic Weighted Kappa, accuracy, and macro-F1, surpassing both ordinal classification and latent generative baselines. t-SNE visualizations further reveal that our method reshapes the latent manifold into compact, grade-ordered clusters with reduced overlap.