E-MRL: Cross-view Aligned Evidence-driven Multimodal Reinforcement Learning for Reliable 3D Tumor Analysis

The article introduces E-MRL (Evidence-driven Multimodal Reinforcement Learning), a novel framework designed to enhance 3D tumor analysis by addressing visual hallucinations in Vision-Language Models. E-MRL operates as a Markov Decision Process focusing on "diagnosis-localization-verification" and incorporates a cross-view consistency reward to ensure semantic alignment between diagnostic reports and visual evidence from 3D CT data. Experimental results on large-scale datasets show that E-MRL outperforms traditional Supervised Fine-Tuning and Reinforcement Learning approaches, improving diagnostic accuracy and reliability for practitioners in medical imaging and AI-driven diagnostics.