# Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer

arXiv: https://arxiv.org/abs/2411.14750 · HTML: https://mlsanity.com/paper/2411.14750/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 22 Nov 2024.

## Builds on (most cited first, 16 of 16)

- [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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Attention-based Deep Multiple Instance Learning](https://mlsanity.com/paper/1802.04712/) (2018)
- [TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification](https://mlsanity.com/paper/2106.00908/) (2021)
- [Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning](https://mlsanity.com/paper/2011.08939/) (2020)
- [Rank consistent ordinal regression for neural networks with application to age estimation](https://mlsanity.com/paper/1901.07884/) (2019)
- [Revisiting Multiple Instance Neural Networks](https://mlsanity.com/paper/1610.02501/) (2016)
- [Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images](https://mlsanity.com/paper/2303.06873/) (2023)
- [Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression](https://mlsanity.com/paper/2103.13629/) (2021)
- [Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology](https://mlsanity.com/paper/2206.01794/) (2022)
- [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)
- [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)
- [Controlling Class Layout for Deep Ordinal Classification via Constrained Proxies Learning](https://mlsanity.com/paper/2303.00396/) (2023)

## Built on by (most cited first, 7 of 7)

- [Learning from Majority Label: A Novel Problem in Multi-class Multiple-Instance Learning](https://mlsanity.com/paper/2509.04023/) (2025)
- [CLoE: Curriculum Learning on Endoscopic Images for Robust MES Classification](https://mlsanity.com/paper/2508.13280/) (2025)
- [Domain Adaptation for Ulcerative Colitis Severity Estimation Using Patient-Level Diagnoses](https://mlsanity.com/paper/2509.14573/) (2025)
- [AGGRNet: Selective Feature Extraction and Aggregation for Enhanced Medical Image Classification](https://mlsanity.com/paper/2511.12382/) (2025)
- [Hypernetwork-Based Adaptive Aggregation for Multimodal Multiple-Instance Learning in Predicting Coronary Calcium Debulking](https://mlsanity.com/paper/2601.21479/) (2026)
- [AnomExpert: Identifying and Selecting Anatomical Planes for Prenatal Ultrasound Anomaly Diagnosis](https://mlsanity.com/paper/2607.13409/) (2026)
- [Towards Whole-Study Screening for Congenital Heart Disease in Fetal Ultrasound Using Multiple Instance Learning](https://mlsanity.com/paper/2609.31376/) (2026)

## Abstract (arXiv)

Patient-level diagnosis of severity in ulcerative colitis (UC) is common in real clinical settings, where the most severe score in a patient is recorded. However, previous UC classification methods (i.e., image-level estimation) mainly assumed the input was a single image. Thus, these methods can not utilize severity labels recorded in real clinical settings. In this paper, we propose a patient-level severity estimation method by a transformer with selective aggregator tokens, where a severity label is estimated from multiple images taken from a patient, similar to a clinical setting. Our method can effectively aggregate features of severe parts from a set of images captured in each patient, and it facilitates improving the discriminative ability between adjacent severity classes. Experiments demonstrate the effectiveness of the proposed method on two datasets compared with the state-of-the-art MIL methods. Moreover, we evaluated our method in real clinical settings and confirmed that our method outperformed the previous image-level methods. The code is publicly available at https://github.com/Shiku-Kaito/Ordinal-Multiple-instance-Learning-for-Ulcerative-Colitis-Severity-Estimation.