# Domain Adaptation for Ulcerative Colitis Severity Estimation Using Patient-Level Diagnoses

arXiv: https://arxiv.org/abs/2509.14573 · HTML: https://mlsanity.com/paper/2509.14573/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Semi-supervised Domain Adaptation via Minimax Entropy](https://mlsanity.com/paper/1904.06487/) (2019)
- [Rank consistent ordinal regression for neural networks with application to age estimation](https://mlsanity.com/paper/1901.07884/) (2019)
- [Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation](https://mlsanity.com/paper/2104.09415/) (2021)
- [Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation](https://mlsanity.com/paper/2111.14353/) (2021)
- [Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation](https://mlsanity.com/paper/2202.05167/) (2022)
- [Automatic Estimation of Ulcerative Colitis Severity from Endoscopy Videos using Ordinal Multi-Instance Learning](https://mlsanity.com/paper/2109.14685/) (2021)
- [Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer](https://mlsanity.com/paper/2411.14750/) (2024)
- [Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data](https://mlsanity.com/paper/2208.03020/) (2022)
- [Disease Severity Regression with Continuous Data Augmentation](https://mlsanity.com/paper/2302.12482/) (2023)

## Abstract (arXiv)

The development of methods to estimate the severity of Ulcerative Colitis (UC) is of significant importance. However, these methods often suffer from domain shifts caused by differences in imaging devices and clinical settings across hospitals. Although several domain adaptation methods have been proposed to address domain shift, they still struggle with the lack of supervision in the target domain or the high cost of annotation. To overcome these challenges, we propose a novel Weakly Supervised Domain Adaptation method that leverages patient-level diagnostic results, which are routinely recorded in UC diagnosis, as weak supervision in the target domain. The proposed method aligns class-wise distributions across domains using Shared Aggregation Tokens and a Max-Severity Triplet Loss, which leverages the characteristic that patient-level diagnoses are determined by the most severe region within each patient. Experimental results demonstrate that our method outperforms comparative DA approaches, improving UC severity estimation in a domain-shifted setting.