M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
It addresses the problem of limited 3D medical image analysis tools for clinicians and researchers, though it is incremental as it extends existing multi-modal large language models to 3D data.
This paper tackles the under-exploration of 3D medical image analysis by introducing a large-scale dataset (M3D-Data with 120K image-text pairs and 662K instruction-response pairs), a versatile model (M3D-LaMed), and a new benchmark (M3D-Bench), resulting in a robust model that outperforms existing solutions.
Medical image analysis is essential to clinical diagnosis and treatment, which is increasingly supported by multi-modal large language models (MLLMs). However, previous research has primarily focused on 2D medical images, leaving 3D images under-explored, despite their richer spatial information. This paper aims to advance 3D medical image analysis with MLLMs. To this end, we present a large-scale 3D multi-modal medical dataset, M3D-Data, comprising 120K image-text pairs and 662K instruction-response pairs specifically tailored for various 3D medical tasks, such as image-text retrieval, report generation, visual question answering, positioning, and segmentation. Additionally, we propose M3D-LaMed, a versatile multi-modal large language model for 3D medical image analysis. Furthermore, we introduce a new 3D multi-modal medical benchmark, M3D-Bench, which facilitates automatic evaluation across eight tasks. Through comprehensive evaluation, our method proves to be a robust model for 3D medical image analysis, outperforming existing solutions. All code, data, and models are publicly available at: https://github.com/BAAI-DCAI/M3D.