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MG-3D: Multi-grained knowledge-enhanced vision-language pre-training for 3D medical image analysis.

Xuefeng Ni1, Linshan Wu2, Jiaxin Zhuang2

  • 1Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China; School of Artificial Intelligence and Robotics, Hunan University, Changsha, China.

Medical Image Analysis
|March 18, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces MG-3D, a novel AI method for 3D medical image analysis. It leverages radiology reports to improve generalization and reduce annotation costs for foundation models (FMs).

Keywords:
3D medical image analysisMultimodal representation learningSelf-supervised learningVision-language pre-training

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Foundation Models

Background:

  • Current AI for 3D medical image analysis struggles with generalization and requires costly task-specific annotations.
  • Foundation models (FMs) offer potential for broad clinical applications with less annotation.
  • Radiology reports, rich in semantics, are underutilized in pre-training FMs for 3D medical imaging.

Purpose of the Study:

  • To develop a vision-language pre-training (VLP) method, MG-3D, to effectively utilize large-scale 3D medical image and radiology report data.
  • To enhance the understanding of multi-grained semantics within and across 3D medical images and their associated reports.
  • To improve the generalization and reduce annotation dependency of AI models in 3D medical image analysis.

Main Methods:

  • MG-3D employs cross-modal global alignment and modality-guided local reconstruction for intra-patient semantic correspondence.
  • Inter-patient visual semantics are correlated using fine-grained report correlations and contrastive learning.
  • Pre-training utilized a large-scale dataset of 47.1K 3D Computed Tomography (CT) scans and reports.

Main Results:

  • MG-3D demonstrated superior transferability, scalability, and generalization across ten diverse clinical tasks.
  • The method showed efficacy on both internal and external validation datasets, including 3D CT and MRI.
  • Evaluations confirmed improved performance in 3D medical image analysis tasks compared to existing methods.

Conclusions:

  • MG-3D effectively harnesses the semantic richness of radiology reports for pre-training foundation models in 3D medical imaging.
  • The proposed approach addresses key limitations in current AI development for medical image analysis, paving the way for more robust and adaptable AI tools.
  • The study highlights the potential of large-scale 3D volume-report data for advancing AI in diagnostics, prognostics, and therapeutics.