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.
@article{arxiv.2410.17241,
title = {Frontiers in Intelligent Colonoscopy},
author = {Ge-Peng Ji and Jingyi Liu and Peng Xu and Nick Barnes and Fahad Shahbaz Khan and Salman Khan and Deng-Ping Fan},
journal= {arXiv preprint arXiv:2410.17241},
year = {2025}
}
Comments
[Work in progress] A comprehensive survey of intelligent colonoscopy in the multimodal era. [Updated Version V2] New training strategy for colonoscopy-specific multimodal language model