Adapting 2D Multi-Modal Large Language Model for 3D CT Image Analysis
3D medical image analysis is of great importance in disease diagnosis and treatment. Recently, multimodal large language models (MLLMs) have exhibited robust perceptual capacity, strong cross-modal alignment, and promising generalizability. Therefore, they have great potential to improve the performance of medical report generation (MRG) and medical visual question answering (MVQA), which serve as two important tasks in clinical scenarios. However, due to the scarcity of 3D medical images, existing 3D medical MLLMs suffer from insufficiently pretrained vision encoder and inability to extract customized image features for different kinds of tasks. In this paper, we propose to first transfe...
| Authors | Yang Yu, Dunyuan Xu, Yaoqian Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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