| --- |
| tags: |
| - pytorch |
| - pathology |
| - flow-matching |
| - dit |
| pipeline_tag: unconditional-image-generation |
| --- |
| |
| This model provides the **pretrained DiT backbone for Agent-GenPath**, supporting pathology image synthesis and downstream segmentation. |
|
|
| The model is introduced in the following paper: |
|
|
| **Agent-Driven Generative Priors for Pathology Image Synthesis and Segmentation** |
|
|
| arXiv: [**2611.06701**](https://arxiv.org/abs/2611.06701) | [PDF](https://arxiv.org/pdf/2611.06701) |
|
|
| Code: [**https://github.com/script-Yang/Agent-GenPath**](https://github.com/script-Yang/Agent-GenPath) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{yang2026agentdrivengenerativepriorspathology, |
| title={Agent-Driven Generative Priors for Pathology Image Synthesis and Segmentation}, |
| author={Sicheng Yang and Rui Tang and Haipeng Zhou and Hongqiu Wang and Zhaohu Xing and Jianda Ma and Lie Dai and Guang Yang and Lei Zhu}, |
| year={2026}, |
| eprint={2611.06701}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV}, |
| url={https://arxiv.org/abs/2611.06701}, |
| } |
| ``` |
