Frontiers in Intelligent Colonoscopy
This work addresses the need for better multimodal AI tools in colonoscopy screening for colorectal cancer, though it appears to be an incremental contribution building on existing techniques.
This study assessed the current landscape of intelligent colonoscopy techniques and identified domain-specific challenges, then established three foundational initiatives including a large-scale multimodal instruction tuning dataset, a colonoscopy-designed multimodal language model, and a multimodal benchmark to advance multimodal research in colonoscopy.
Colonoscopy is currently one of the most sensitive screening methods for colorectal cancer. This study investigates the frontiers of intelligent colonoscopy techniques and their prospective implications for multimodal medical applications. With this goal, we begin by assessing the current data-centric and model-centric landscapes through four tasks for colonoscopic scene perception, including classification, detection, segmentation, and vision-language understanding. This assessment enables us to identify domain-specific challenges and reveals that multimodal research in colonoscopy remains open for further exploration. To embrace the coming multimodal era, we establish three foundational initiatives: a large-scale multimodal instruction tuning dataset ColonINST, a colonoscopy-designed multimodal language model ColonGPT, and a multimodal benchmark. To facilitate ongoing monitoring of this rapidly evolving field, we provide a public website for the latest updates: https://github.com/ai4colonoscopy/IntelliScope.