Colon-X: Advancing Intelligent Colonoscopy from Multimodal Understanding to Clinical Reasoning

• URL: http://arxiv.org/abs/2512.03667v1
• Date: Wed, 03 Dec 2025 10:55:07 GMT
• Title: Colon-X: Advancing Intelligent Colonoscopy from Multimodal Understanding to Clinical Reasoning
• Authors: Ge-Peng Ji, Jingyi Liu, Deng-Ping Fan, Nick Barnes,
• Abstract summary: Colon-X is an open initiative aimed at advancing multimodal intelligence in colonoscopy.<n>ColonVQA is the most comprehensive multimodal dataset ever built for colonoscopy.<n>ColonReason is a reasoning dataset annotated through a multi-expert debating pipeline.<n>ColonR1 is the first R1-styled model incorporating task-adaptive rewarding and gradient-stable optimization techniques.
• Score: 45.385273103646654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, we present Colon-X, an open initiative aimed at advancing multimodal intelligence in colonoscopy. We begin by constructing ColonVQA, the most comprehensive multimodal dataset ever built for colonoscopy, featuring over 1.1M+ visual question answering entries across 76 clinical findings and 18 multimodal tasks. Beyond serving as a community-wide data foundation, we further investigate a critical yet underexplored transition in colonoscopy - evolving from multimodal understanding to clinical reasoning: (a) To capture the current landscape of multimodal understanding behaviors, we systematically assess the generalizability of 22 multimodal large language models and examine their reliability under human-induced perturbations. The results reveal that clinical outputs from leading MLLMs remain far from robust and trustworthy. (b) To narrow this gap, we further explore reasoning-centric intelligence tailored for colonoscopy. Specifically, we curate ColonReason, a clinically grounded reasoning dataset annotated through a multi-expert debating pipeline, and develop ColonR1, the first R1-styled model incorporating task-adaptive rewarding and gradient-stable optimization techniques. Under data-scarce conditions, our ColonR1 achieves 56.61% overall accuracy, outperforming supervised fine-tuning by 25.22%, and sets a new reasoning-enabled baseline for multimodal colonoscopy analysis. All data and model resources are publicly available at https://github.com/ai4colonoscopy/Colon-X.

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