T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency

While 3D visual self-supervised learning (vSSL) shows promising results in capturing visual representations, it overlooks the clinical knowledge from radiology reports. Meanwhile, 3D medical vision-language pre-training (MedVLP) remains underexplored due to the lack of a large-scale, publicly available 3D medical image-report dataset. To bridge this gap, we introduce **CT-3DVLP**, the first and largest **public** 3D volume-report dataset, establishing a comprehensive benchmark for 3D MedVLP research. Meanwhile, we propose the **T3D** framework, which enhances 3D MedVLP beyond naive CLIP-style alignment that directly pairs volumes with reports but neglects local visual representations. Instead, we introduce **Text-informed Multi-view Alignment (TMA)**, a novel approach that clusters volumetric data while enforcing consistency across different views of the same volume-report pair. TMA integrates textual features into fine-grained visual representations, ensuring contextual coherence across views. We evaluate T3D across multiple downstream tasks in both unimodal and cross-modal settings, including zero-shot and fine-tuned classification, cross-modal retrieval, report generation, and semantic segmentation. Our results show that T3D consistently outperforms existing vSSL and multimodal methods, demonstrating superior zero-shot and fine-tuning capabilities and setting a new benchmark for 3D medical image understanding.

@article{liu2025_2312.01529,
  title={ T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency },
  author={ Che Liu and Cheng Ouyang and Yinda Chen and Cesar César Quilodrán-Casas and Lei Ma and Jie Fu and Yike Guo and Anand Shah and Wenjia Bai and Rossella Arcucci },
  journal={arXiv preprint arXiv:2312.01529},
  year={ 2025 }
}