license: cc-by-nc-nd-4.0
language:
- en
tags:
- Pathology
- arxiv:2505.11404
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[Arxiv] | [Github Repo] | [Cite]
Introduction📝
To bridge the gap between fine-grained tissue morphology and clinical semantic understanding in pathology, we present Patho-CLIP-L, a vision-language model tailored for high-resolution cross-modal representation learning in pathological diagnosis.
Patho-CLIP-L is built on the OpenAI-CLIP-L architecture and trained through a two-stage progressive paradigm:
Stage I: Contrastive pretraining on PathGen-1.6M, focusing on cell morphology and tissue organization to embed high-resolution visual priors
Stage II: Joint training on a 3.5M composite corpus comprising PathGen-1.6M, Quilt-1M, PathCap, and a textbook-derived dataset, to integrate domain-specific semantics with morphological features
This strategy enables Patho-CLIP-L to achieve strong performance in semantic alignment, cross-modal retrieval, and tissue-level discrimination, offering a robust foundation for downstream pathology tasks.
Acknowledgements🎖
We gratefully acknowledge the OpenCLIP project for providing an efficient and extensible implementation of CLIP models. Its flexible training pipeline, model support, and strong community contributions significantly facilitated the development and training of our Patho-CLIP-L model.
We thank the authors and maintainers for their excellent work.
Citation❤️
If you find our work helpful, a citation would be greatly appreciated:
@article{zhang2025patho,
title={Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner},
author={Zhang, Wenchuan and Zhang, Penghao and Guo, Jingru and Cheng, Tao and Chen, Jie and Zhang, Shuwan and Zhang, Zhang and Yi, Yuhao and Bu, Hong},
journal={arXiv preprint arXiv:2505.11404},
year={2025}
}