File size: 1,046 Bytes
9cd0c69 b8a777d 9cd0c69 c462849 af6f51b c462849 9af9cdd af6f51b b8a777d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | ---
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},
}
``` |