arXiv:2611.04624v1 Announce Type: cross
Abstract: A key challenge in colon capsule endoscopy (CCE) analysis is accurately identifying whether multiple images depict the same polyp, which is critical to avoid over-counting and unnecessary clinical interventions. Additionally, this prevents prolonged follow-up colonoscopies where endoscopists search for duplicate polyps without precise localization. This study presents a novel contrastive learning-based approach to polyp matching using a Siamese neural network architecture. The model was trained on a dataset from the Danish CareForColon2015 trial. The dataset comprises 5,103 image pairs of confirmed same polyp and 5,103 pairs of different polyps, annotated by experienced clinicians. The proposed model employs ResNet variants (ResNet18, ResNet34, ResNet50), DenseNet121 and EfficientNet B0/B3 as backbones and utilizes contrastive learning with cosine vector distance metric for classification. Experimental results demonstrate high classification accuracies of up to 92% and AUC values close to 0.98. These results suggest that automated polyp matching has the potential to reduce polyp over-counting, improve patient risk stratification, and reduce unnecessary follow-up colonoscopies.
arxiv
Deep Learning Polyp Matching for Colon Capsule Endoscopy
This study introduces a contrastive learning-based Siamese neural network for matching polyps across colon capsule endoscopy images, trained on 5,103 same-polyp and 5,103 different-polyp pairs from the Danish CareForColon2015 trial. Using ResNet, DenseNet, and EfficientNet backbones with cosine distance, the model achieves up to 92% accuracy and AUC near 0.98. The approach aims to reduce polyp over-counting and unnecessary follow-up colonoscopies.
Provides a concrete, recent AI development with strong performance metrics and clear clinical utility in medical imaging.
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