CLoE: Curriculum Learning on Endoscopic Images for Robust MES Classification
This work addresses robust disease severity estimation in ulcerative colitis for medical imaging applications, representing an incremental improvement through a novel training strategy.
The paper tackles the challenge of Mayo Endoscopic Subscore (MES) classification from endoscopic images, which is hindered by label noise and ordinal structure, by proposing CLoE, a curriculum learning framework that improves performance, achieving 82.5% accuracy and a QWK of 0.894 on the LIMUC dataset.
Estimating disease severity from endoscopic images is essential in assessing ulcerative colitis, where the Mayo Endoscopic Subscore (MES) is widely used to grade inflammation. However, MES classification remains challenging due to label noise from inter-observer variability and the ordinal nature of the score, which standard models often ignore. We propose CLoE, a curriculum learning framework that accounts for both label reliability and ordinal structure. Image quality, estimated via a lightweight model trained on Boston Bowel Preparation Scale (BBPS) labels, is used as a proxy for annotation confidence to order samples from easy (clean) to hard (noisy). This curriculum is further combined with ResizeMix augmentation to improve robustness. Experiments on the LIMUC and HyperKvasir datasets, using both CNNs and Transformers, show that CLoE consistently improves performance over strong supervised and self-supervised baselines. For instance, ConvNeXt-Tiny reaches 82.5\% accuracy and a QWK of 0.894 on LIMUC with low computational cost. These results highlight the potential of difficulty-aware training strategies for improving ordinal classification under label uncertainty. Code will be released at https://github.com/zeynepozdemir/CLoE.