Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs

Multimodal Large Language Models (MLLMs) have demon-strated remarkable potential in medical image analysis. However, theirapplication in gastrointestinal endoscopy is currently hindered by twocritical limitations: the misalignment between general model reasoningand standardized clinical cognitive pathways, and the lack of causal as-sociation between visual features and diagnostic outcomes. In this paper,we propose a novel Clinical-Cognitive-Aligned (CogAlign) framework toaddress these challenges. First, we endow the model with rigorous clinicalanalytical capabilities by constructing the hierarchical clinical cognitiondataset and employing Supervised Fine-Tuning (SFT). Unlike conven-tional approaches, this strategy internalizes the hierarchical diagnosticlogic of experts, ranging from anatomical localization and morphologicalevaluation to microvascular analysis, directly into the model. Second, toeliminate visual bias, we provide a theoretical analysis demonstratingthat standard supervised tuning inevitably converges to spurious back-ground correlations. Guided by this insight, we propose a counterfactual-driven reinforcement learning strategy to enforce causal rectification. Bygenerating counterfactual normal samples via lesion masking and opti-mizing through clinical-cognition-centric rewards, we constrain the modelto strictly ground its diagnosis in causal lesion features. Extensive exper-iments demonstrate that our approach achieves State-of-the-Art (SoTA)performance across multiple benchmarks, significantly enhancing diag-nostic accuracy in complex clinical scenarios.