MedM-VL: What Makes a Good Medical LVLM?
This work addresses the need for robust medical LVLMs to improve healthcare diagnostics, though it appears incremental as it builds on existing frameworks.
The study tackled the challenge of developing effective medical large vision-language models (LVLMs) for tasks like report generation and visual question answering by systematically exploring architectures and training strategies based on the LLaVA framework, resulting in the release of a modular codebase and pre-trained models for 2D and 3D medical image analysis.
Medical image analysis is essential in modern healthcare. Deep learning has redirected research focus toward complex medical multimodal tasks, including report generation and visual question answering. Traditional task-specific models often fall short in handling these challenges. Large vision-language models (LVLMs) offer new solutions for solving such tasks. In this study, we build on the popular LLaVA framework to systematically explore model architectures and training strategies for both 2D and 3D medical LVLMs. We present extensive empirical findings and practical guidance. To support reproducibility and future research, we release a modular codebase, MedM-VL, and two pre-trained models: MedM-VL-2D for 2D medical image analysis and MedM-VL-CT-Chest for 3D CT-based applications. The code is available at: https://github.com/MSIIP/MedM-VL