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.
Related papers
- CT-GLIP: 3D Grounded Language-Image Pretraining with CT Scans and Radiology Reports for Full-Body Scenarios [53.94122089629544]
We introduce CT-GLIP (Grounded Language-Image Pretraining with CT scans), a novel method that constructs organ-level image-text pairs to enhance multimodal contrastive learning.
Our method, trained on a multimodal CT dataset comprising 44,011 organ-level vision-text pairs from 17,702 patients across 104 organs, demonstrates it can identify organs and abnormalities in a zero-shot manner using natural languages.
arXiv Detail & Related papers (2024-04-23T17:59:01Z) - Generative Enhancement for 3D Medical Images [74.17066529847546]
We propose GEM-3D, a novel generative approach to the synthesis of 3D medical images.
Our method begins with a 2D slice, noted as the informed slice to serve the patient prior, and propagates the generation process using a 3D segmentation mask.
By decomposing the 3D medical images into masks and patient prior information, GEM-3D offers a flexible yet effective solution for generating versatile 3D images.
arXiv Detail & Related papers (2024-03-19T15:57:04Z) - Med3DInsight: Enhancing 3D Medical Image Understanding with 2D
Multi-Modal Large Language Models [1.64647940449869]
Existing 3D convolution and transformer-based methods have limited semantic understanding of an image volume.
We propose Med3DInsight, which marries existing 3D image encoders with 2D MLLMs and bridges them via a Plane-Slice-Aware Transformer (PSAT) module.
arXiv Detail & Related papers (2024-03-08T08:15:53Z) - T3D: Towards 3D Medical Image Understanding through Vision-Language
Pre-training [33.548818136506334]
We introduce T3D, the first framework designed for high-resolution 3D medical images.
T3D incorporates two text-informed pretext tasks: (lowerromannumeral1) text-informed contrastive learning; (lowerromannumeral2) text-informed image restoration.
T3D significantly outperforms current vSSL methods in tasks like organ and tumor segmentation, as well as disease classification.
arXiv Detail & Related papers (2023-12-03T23:03:22Z) - 3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in
Radiology [6.851500027718433]
The field of 3D medical image retrieval is still emerging, lacking established evaluation benchmarks, comprehensive datasets, and thorough studies.
This paper introduces a novel benchmark for 3D Medical Image Retrieval (3D-MIR) that encompasses four different anatomies imaged with computed tomography.
Using this benchmark, we explore a diverse set of search strategies that use aggregated 2D slices, 3D volumes, and multi-modal embeddings from popular multi-modal foundation models as queries.
arXiv Detail & Related papers (2023-11-23T00:57:35Z) - JM3D & JM3D-LLM: Elevating 3D Understanding with Joint Multi-modal Cues [68.76032126906743]
We introduce JM3D, a comprehensive approach integrating point cloud, text, and image.
Key contributions include the Structured Multimodal Organizer (SMO), enriching vision-language representation with multiple views and hierarchical text.
Our advanced model, JM3D-LLM, marries 3D representation with large language models via efficient fine-tuning.
arXiv Detail & Related papers (2023-10-14T06:13:20Z) - Towards Generalist Foundation Model for Radiology by Leveraging
Web-scale 2D&3D Medical Data [66.9359934608229]
This study aims to initiate the development of Radiology Foundation Model, termed as RadFM.
To the best of our knowledge, this is the first large-scale, high-quality, medical visual-language dataset, with both 2D and 3D scans.
We propose a new evaluation benchmark, RadBench, that comprises five tasks, including modality recognition, disease diagnosis, visual question answering, report generation and rationale diagnosis.
arXiv Detail & Related papers (2023-08-04T17:00:38Z) - 3D Matting: A Soft Segmentation Method Applied in Computed Tomography [26.25446145993599]
Three-dimensional (3D) images, such as CT, MRI, and PET, are common in medical imaging applications and important in clinical diagnosis.
Semantic ambiguity is a typical feature of many medical image labels.
In 2D medical images, using soft masks instead of binary masks generated by image matting to characterize lesions can provide rich semantic information.
arXiv Detail & Related papers (2022-09-16T10:18:59Z) - MedMNIST v2: A Large-Scale Lightweight Benchmark for 2D and 3D
Biomedical Image Classification [59.10015984688104]
MedMNIST v2 is a large-scale MNIST-like dataset collection of standardized biomedical images.
The resulting dataset consists of 708,069 2D images and 10,214 3D images in total.
arXiv Detail & Related papers (2021-10-27T22:02:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.