CVPR 2025: VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge

CVPR 2025: VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge

VILA-M3 addresses critical limitations in applying generalist vision-language models (VLMs) to medical imaging tasks. The paper argues that while large-scale VLMs like Gemini and GPT-4o perform well in general domains, they lack the nuanced domain expertise required for clinical applications. VILA-M3 introduces a new framework that incorporates an additional instruction fine-tuning stage guided by domain expert models—specialized AI systems trained for tasks like tumor detection and anatomical segmentation. By integrating expert feedback during both training and inference, VILA-M3 enables more precise handling of complex medical imaging challenges such as segmentation, classification, report generation, and visual question answering.

Empirical results demonstrate that VILA-M3 outperforms previous state-of-the-art models, including Med-Gemini, achieving up to 9% improvement over Med-Gemini and 6% over task-specific models across multiple benchmarks. The framework leverages both 2D and 3D medical expert models and emphasizes dataset balancing and dynamic expert integration, which enhances model generalization and reliability for real-world clinical scenarios. The VILA-M3 framework is open source, and the results highlight the value of embedding medical expert knowledge directly within VLMs to improve precision, reliability, and applicability in healthcare settings

Paper: https://arxiv.org/abs/2411.12915

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