---
license: apache-2.0
base_model:
- Qwen/Qwen2-VL-2B-Instruct
language:
- en
---
<div align="center">
<h1>
  MedVLM-R1
</h1>
</div>

<div align="center">
 <a href="https://arxiv.org/abs/2502.19634" target="_blank">Paper</a>
</div>

# <span id="Start">Introduction</span>
MedVLM-R1 is a medical Vision-Language Model built upon [Qwen2-VL-2B](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) and fine-tuned using the [GRPO](https://arxiv.org/abs/2402.03300) reinforcement learning framework. Trained on 600 MRI VQA samples from the [HuatuoGPT-Vision dataset](https://huggingface.co/datasets/FreedomIntelligence/Medical_Multimodal_Evaluation_Data), MedVLM-R1 excels in out-of-distribution performance on CT and X-ray VQA tasks. It also demonstrates explicit medical reasoning capabilities beyond merely providing final answers, ensuring greater interpretability and trustworthiness in clinical applications.

# <span id="Start">Quick Start</span>

### 1. Load the model
```python
from transformers import Qwen2VLForConditionalGeneration, AutoProcessor, GenerationConfig
from qwen_vl_utils import process_vision_info
import torch

MODEL_PATH = 'JZPeterPan/MedVLM-R1'

model = Qwen2VLForConditionalGeneration.from_pretrained(
    MODEL_PATH,
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

processor = AutoProcessor.from_pretrained(MODEL_PATH)

temp_generation_config = GenerationConfig(
    max_new_tokens=1024,
    do_sample=False,  
    temperature=1, 
    num_return_sequences=1,
    pad_token_id=151643,
)
```
### 2. Load the VQA Data
Pick one of the following examples. These are samples from [OmniMedVQA](https://huggingface.co/datasets/foreverbeliever/OmniMedVQA) data and are bundled by [HuatuoGPT-Vision](https://huggingface.co/datasets/FreedomIntelligence/Medical_Multimodal_Evaluation_Data).

```python
question = {"image": ['images/successful_cases/mdb146.png'], "problem": "What content appears in this image?\nA) Cardiac tissue\nB) Breast tissue\nC) Liver tissue\nD) Skin tissue", "solution": "B", "answer": "Breast tissue"}

question = {"image": ["images/successful_cases/person19_virus_50.jpeg"], "problem": "What content appears in this image?\nA) Lungs\nB) Bladder\nC) Brain\nD) Heart", "solution": "A", "answer": "Lungs"}

question = {"image":["images/successful_cases/abd-normal023599.png"],"problem":"Is any abnormality evident in this image?\nA) No\nB) Yes.","solution":"A","answer":"No"}

question = {"image":["images/successful_cases/foot089224.png"],"problem":"Which imaging technique was utilized for acquiring this image?\nA) MRI\nB) Electroencephalogram (EEG)\nC) Ultrasound\nD) Angiography","solution":"A","answer":"MRI"}

question = {"image":["images/successful_cases/knee031316.png"],"problem":"What can be observed in this image?\nA) Chondral abnormality\nB) Bone density loss\nC) Synovial cyst formation\nD) Ligament tear","solution":"A","answer":"Chondral abnormality"}

question = {"image":["images/successful_cases/shoulder045906.png"],"problem":"What can be visually detected in this picture?\nA) Bone fracture\nB) Soft tissue fluid\nC) Blood clot\nD) Tendon tear","solution":"B","answer":"Soft tissue fluid"}

question = {"image":["images/successful_cases/brain003631.png"],"problem":"What attribute can be observed in this image?\nA) Focal flair hyperintensity\nB) Bone fracture\nC) Vascular malformation\nD) Ligament tear","solution":"A","answer":"Focal flair hyperintensity"}

question = {"image":["images/successful_cases/mrabd005680.png"],"problem":"What can be observed in this image?\nA) Pulmonary embolism\nB) Pancreatic abscess\nC) Intraperitoneal mass\nD) Cardiac tamponade","solution":"C","answer":"Intraperitoneal mass"}
```
### 3. Run the inference

```python
QUESTION_TEMPLATE = """
    {Question} 
    Your task: 
    1. Think through the question step by step, enclose your reasoning process in <think>...</think> tags. 
    2. Then provide the correct single-letter choice (A, B, C, D,...) inside <answer>...</answer> tags.
    3. No extra information or text outside of these tags.
    """

message = [{
    "role": "user",
    "content": [{"type": "image", "image": f"file://{question['image'][0]}"}, {"type": "text","text": QUESTION_TEMPLATE.format(Question=question['problem'])}]
}]

text = processor.apply_chat_template(message, tokenize=False, add_generation_prompt=True)
    
image_inputs, video_inputs = process_vision_info(message)
inputs = processor(
    text=text,
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
).to("cuda")

generated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=1024, do_sample=False, generation_config=temp_generation_config)

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(f'model output: {output_text[0]}')

```
### Failure cases
MedVLM-R1's reasoning fails when testing on more difficult VQA examples. Although it can output correct choices in the following examples, the reasoning of them is either superficial or contradictory. 
```python
question = {"image":["images/failure_cases/mrabd021764.png"],"problem":"What is the observable finding in this image?\nA) Brain lesion\nB) Intestinal lesion\nC) Gallbladder lesion\nD) Pancreatic lesion","solution":"D","answer":"Pancreatic lesion"}

question = {"image":["images/failure_cases/spine010017.png"],"problem":"What can be observed in this image?\nA) Cystic lesions\nB) Fractured bones\nC) Inflamed tissue\nD) Nerve damage","solution":"A","answer":"Cystic lesions"}

question = {"image":["images/failure_cases/ankle056120.png"],"problem":"What attribute can be observed in this image?\nA) Bursitis\nB) Flexor pathology\nC) Tendonitis\nD) Joint inflammation","solution":"B","answer":"Flexor pathology"}

question = {"image":["images/failure_cases/lung067009.png"],"problem":"What is the term for the anomaly depicted in the image?\nA) Pulmonary embolism\nB) Airspace opacity\nC) Lung consolidation\nD) Atelectasis","solution":"B","answer":"Airspace opacity"}

```

# <span id="Start">Acknowledgement</span>
We thank all machine learning / medical workers for making public codebase / datasets available to the community 🫶🫶🫶

If you find our work helpful, feel free to give us a cite.

```
@article{pan2025medvlm,
  title={MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning},
  author={Pan, Jiazhen and Liu, Che and Wu, Junde and Liu, Fenglin and Zhu, Jiayuan and Li, Hongwei Bran and Chen, Chen and Ouyang, Cheng and Rueckert, Daniel},
  journal={arXiv preprint arXiv:2502.19634},
  year={2025}
}
```