Frontiers in Intelligent Colonoscopy
Authors: name: Ge-Peng Ji, name: Jingyi Liu, name: Peng Xu, name: Nick Barnes, name: Fahad Shahbaz Khan, name: Salman Khan, name: Deng-Ping Fan

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.