Endo-CLIP: Progressive Self-Supervised Pre-training on Raw Colonoscopy Records
This work addresses challenges in endoscopic image analysis for medical applications, representing an incremental advancement in domain-specific pre-training.
The paper tackled the problem of pre-training on colonoscopy image-text records by introducing Endo-CLIP, a self-supervised framework that improved polyp detection and classification, significantly outperforming state-of-the-art methods in zero-shot and few-shot settings.
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.