M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models

• URL: http://arxiv.org/abs/2404.00578v1
• Date: Sun, 31 Mar 2024 06:55:12 GMT
• Title: M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
• Authors: Fan Bai, Yuxin Du, Tiejun Huang, Max Q. -H. Meng, Bo Zhao,
• Abstract summary: Previous research has primarily focused on 2D medical images, leaving 3D images under-explored, despite their richer spatial information. We present a large-scale 3D multi-modal medical dataset, M3D-Data, comprising 120K image-text pairs and 662K instruction-response pairs. We also introduce a new 3D multi-modal medical benchmark, M3D-Bench, which facilitates automatic evaluation across eight tasks.
• Score: 49.5030774873328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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