# Learning Disease State from Noisy Ordinal Disease Progression Labels

arXiv: https://arxiv.org/abs/2503.10440 · HTML: https://mlsanity.com/paper/2503.10440/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 13 Mar 2025.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rank consistent ordinal regression for neural networks with application to age estimation](https://mlsanity.com/paper/1901.07884/) (2019)
- [Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities](https://mlsanity.com/paper/2111.08851/) (2021)
- [Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning](https://mlsanity.com/paper/1910.09420/) (2019)
- [TINC: Temporally Informed Non-Contrastive Learning for Disease Progression Modeling in Retinal OCT Volumes](https://mlsanity.com/paper/2206.15282/) (2022)
- [Robust Deep Ordinal Regression Under Label Noise](https://mlsanity.com/paper/1912.03488/) (2019)
- [Class Distance Weighted Cross Entropy Loss for Classification of Disease Severity](https://mlsanity.com/paper/2412.01246/) (2024)
- [OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels](https://mlsanity.com/paper/2503.02899/) (2025)
- [Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)](https://mlsanity.com/paper/2405.09549/) (2024)
- [A generalized framework to predict continuous scores from medical ordinal labels](https://mlsanity.com/paper/2305.19097/) (2023)

## Built on by (most cited first, 1 of 1)

- [Ordinal Diffusion Models for Color Fundus Images](https://mlsanity.com/paper/2602.24013/) (2026)

## Abstract (arXiv)

Learning from noisy ordinal labels is a key challenge in medical imaging. In this work, we ask whether ordinal disease progression labels (better, worse, or stable) can be used to learn a representation allowing to classify disease state. For neovascular age-related macular degeneration (nAMD), we cast the problem of modeling disease progression between medical visits as a classification task with ordinal ranks. To enhance generalization, we tailor our model to the problem setting by (1) independent image encoding, (2) antisymmetric logit space equivariance, and (3) ordinal scale awareness. In addition, we address label noise by learning an uncertainty estimate for loss re-weighting. Our approach learns an interpretable disease representation enabling strong few-shot performance for the related task of nAMD activity classification from single images, despite being trained only on image pairs with ordinal disease progression labels.