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| Config | |
|---|---|
| Image encoder | google/siglip-base-patch16-256-multilingual |
| Connector | Cross-Attention + MLP (2-layer) |
| LLM | Qwen/Qwen2.5-3B-Instruct |
| Image resolution | 32*256*256 |
| Sequence length | 2048 |
| Task | CT-CHAT | MedM-VL-CT-Chest (3D) | MedM-VL-CT-Chest (2D+Avg) | MedM-VL-CT-Chest (2D+Attn) |
|---|---|---|---|---|
| Long answer | 0.482 | 0.619 | 0.622 | 0.623 |
| Short answer | 0.274 | 0.658 | 0.664 | 0.667 |
| Multiple choice | 0.838 | 0.924 | 0.920 | 0.925 |
| Report generation | 0.395 | 0.419 | 0.441 | 0.439 |
1@inproceedings{shi2025medm,
2 title={Medm-vl: What makes a good medical lvlm?},
3 author={Shi, Yiming and Yang, Shaoshuai and Zhu, Xun and Wang, Haoyu and Fu, Xiangling and Li, Miao and Wu, Ji},
4 booktitle={International Workshop on Agentic AI for Medicine},
5 pages={290--299},
6 year={2025},
7 organization={Springer}
8}