Maverick: A Multi-Agent VLM Evaluation and Optimization Framework for Medical Imaging

Maverick is an open-source modular multi-agent pipeline framework designed specifically to evaluate and improve medical image descriptions generated by Vision-Language Models (VLMs), aiming to enhance the accuracy and reliability of medical AI. As a master's thesis project, it provides a systematic solution for medical AI quality control through multi-agent collaboration mechanisms.

Vision-Language Models (VLMs) have made significant progress in general image understanding, but face professional challenges in the medical imaging field: medical images involve complex anatomical structures, pathological features, and clinical semantics, requiring extremely high accuracy and completeness in descriptions. Traditional VLM evaluation methods struggle to capture subtle differences in medical scenarios, leading to potentially misleading or incomplete generated descriptions.

The core innovation of Maverick lies in its multi-agent collaboration mechanism, which includes several specialized agents: the Content Accuracy Evaluation Agent verifies the correctness of medical terms and pathological descriptions; the Completeness Check Agent ensures coverage of key regions and features; the Clinical Relevance Agent assesses the degree to which descriptions support clinical decision-making; the Language Quality Agent focuses on the clarity and professionalism of descriptions. These agents collaborate in a pipeline to form a comprehensive evaluation system.

Maverick's workflow consists of two phases: In the evaluation phase, it receives medical image descriptions generated by VLMs, and each agent performs specialized assessments in parallel to generate multi-dimensional quality scores and detailed feedback; in the optimization phase, based on the evaluation results, it guides iterative improvements of VLMs through a feedback loop. This closed-loop design continuously enhances the quality of medical image descriptions.

Maverick is an open-source project implemented in Python. Its modular design allows researchers to customize evaluation strategies for specific medical fields such as radiology and pathology, and it is easy to integrate with mainstream VLM frameworks. The open-source nature promotes community collaboration, helps establish industry standards for VLM evaluation in medical imaging, and provides a valuable research tool for the medical AI community.

With the application of models like GPT-4V and Med-Gemini in the medical imaging field, ensuring the accuracy and safety of generated content has become an urgent issue. Maverick's evaluation framework can be used in academic research and as a quality control tool for medical AI products, helping developers identify potential risks before deployment.

Maverick represents an important step forward for medical imaging AI towards trustworthy AI. Through multi-agent collaboration and systematic evaluation, it provides a feasible path to enhance the performance of VLMs in medical scenarios, making it a framework worthy of in-depth research and application by medical AI R&D engineers and researchers.