README.md

---
license: cc-by-nc-nd-4.0
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- multimodal
- Pathology
- arxiv:2505.11404
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---
# Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner
\[[Arxiv](https://arxiv.org/abs/2505.11404)\] | \[[Github Repo](https://github.com/Wenchuan-Zhang/Patho-R1)] | \[[Cite](#citation❤️)\]

## Introduction📝
While vision-language models have shown impressive progress in general medical domains, pathology remains a challenging subfield due to its high-resolution image requirements and complex diagnostic reasoning.  

To address this gap, we introduce **Patho-R1-7B**, a multimodal pathology reasoner designed to enhance diagnostic understanding through structured reasoning. **Patho-R1-7B** is trained using a three-stage pipeline:  
1. *Continued pretraining* on **3.5M pathology figure-caption pairs** for domain knowledge acquisition  
2. *Supervised fine-tuning* on **500k expert-annotated Chain-of-Thought samples** to encourage reasoning  
3. *Reinforcement learning* with **Group Relative Policy Optimization** to refine response quality  

Experimental results show that **Patho-R1-7B** achieves strong performance across key pathology tasks, including **multiple choice questions** and **visual question answering**, highlighting its potential for real-world pathology AI applications.
![Patho-R1 Overview](https://github.com/Wenchuan-Zhang/Patho-R1/raw/main/docs/overview.png)

### Quickstart🏃
Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:

```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info


model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "WenchuanZhang/Patho-R1-7B",
    torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("WenchuanZhang/Patho-R1-7B")

# example question from Pathmmu-test-dataset
# ground truth: D
# Reasoning style options (choose one):
# - Chain-of-Draft, a concise reasoning prompting strategy (COD):
# You are a pathology expert, your task is to think step by step, but only keep a minimum draft for each thinking step, with 5 words at most. Return the answer at the end of the response after a separator. Use the following format:<think> Your step-by-step reasoning </think><answer> Your final answer </answer>
# - Chain-of-Thought (COT):
messages = [
    {   "role": "system",
        "content": "You are a pathology expert, your task is to answer question step by step. Use the following format:<think> Your step-by-step reasoning </think><answer> Your final answer </answer>"},
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "./images/example.jpg",
            },
            {"type": "text", "text": "What feature in the provided micrograph is indicative of chronic inflammation? /n A. Granuloma formation /n B. Multinucleated giant cells /n C. Neutrophilic infiltration /n D. Plasma cells with eccentrically placed nuclei"},       
        ],
    }
]
# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to(model.device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=2048)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
## Acknowledgements🎖

We gratefully acknowledge the contributions of the open-source community, particularly the following projects which laid the foundation for various components of this work:

- [Qwen](https://github.com/QwenLM) for providing powerful vision language models that significantly advanced our multimodal understanding and generation capabilities.  
- [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO) for document layout detection.  
- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) for comprehensive optical character recognition.  
- [ModelScope Swift](https://github.com/modelscope/ms-swift) for efficient model serving and deployment tools.  
- [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) for robust LLM training and fine-tuning pipelines.  
- [VERL](https://github.com/volcengine/verl) for valuable visual-language pretraining resources.  
- [DeepSeek](https://github.com/deepseek-ai) for high-quality models and infrastructure supporting text understanding.

We thank the authors and contributors of these repositories for their dedication and impactful work, which made our development of Patho-R1-3B possible.

## 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}
}
```