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arXiv · 2505.09435

Endo-CLIP: Progressive Self-Supervised Pre-training on Raw Colonoscopy Records

Yili He·Yan Zhu·Peiyao Fu·Ruijie Yang·Tianyi Chen·Zhihua Wang·Quanlin Li·Pinghong Zhou·Xian Yang·Shuo Wang
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Abstract

Pre-training on image-text colonoscopy records offers substantial potential for improving endoscopic image analysis, but faces challenges including non-informative background images, complex medical terminology, and ambiguous multi-lesion descriptions. We introduce Endo-CLIP, a novel self-supervised framework that enhances Contrastive Language-Image Pre-training (CLIP) for this domain. Endo-CLIP's three-stage framework--cleansing, attunement, and unification--addresses these challenges by (1) removing background frames, (2) leveraging large language models to extract clinical attributes for fine-grained contrastive learning, and (3) employing patient-level cross-attention to resolve multi-polyp ambiguities. Extensive experiments demonstrate that Endo-CLIP significantly outperforms state-of-the-art pre-training methods in zero-shot and few-shot polyp detection and classification, paving the way for more accurate and clinically relevant endoscopic analysis.

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Yili He, Yan Zhu, Peiyao Fu, Ruijie Yang, Tianyi Chen, Zhihua Wang, Quanlin Li, Pinghong Zhou, Xian Yang, Shuo Wang. 2025-05-14. Endo-CLIP: Progressive Self-Supervised Pre-training on Raw Colonoscopy Records. https://arxiv.org/abs/2505.09435

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