README.md

---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
tags:
  - medical
  - multimodal
  - grounding
  - report-generation
  - radiology
  - clinical-reasoning
  - mri
  - ct
  - histopathology
  - x-ray
  - fundus
---

# MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images

[![Paper](https://img.shields.io/badge/arXiv-Paper-red)](https://arxiv.org/abs/2602.06965)
[![Model](https://img.shields.io/badge/🤗-MedMO--8B--Next-blue)](https://huggingface.co/MBZUAI/MedMO-8B-Next)
[![Model](https://img.shields.io/badge/🤗-MedMO--8B-blue)](https://huggingface.co/MBZUAI/MedMO-8B)
[![Model](https://img.shields.io/badge/🤗-MedMO--4B-blue)](https://huggingface.co/MBZUAI/MedMO-4B)
[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)


<p align="center">
  <img src="MedMO-logo.png" alt="MedMO Logo" width="300"/>
</p>


**MedMO** is a powerful open-source multimodal foundation model designed for comprehensive medical image understanding and grounding. Built on Qwen3-VL architecture and trained on 26M+ diverse medical samples across 45 datasets, MedMO achieves state-of-the-art performance across multiple medical imaging tasks.

## 🎯 Capabilities

MedMO excels at a comprehensive range of medical imaging tasks:

- **Visual Question Answering (VQA)**: Answer complex questions about medical images across radiology, pathology, ophthalmology, and dermatology
- **Text-Based Medical QA**: Clinical reasoning and medical knowledge question answering
- **Radiology Report Generation**: Generate detailed, clinically accurate radiology reports from medical images
- **Disease Localization with Bounding Boxes**: Precise spatial detection and localization of pathological findings
- **Anatomical Grounding**: Spatial localization and grounding of anatomical structures
- **Clinical Reasoning**: Step-by-step diagnostic reasoning and clinical decision support
- **Diagnostic Classification**: Multi-class disease classification across diverse imaging modalities
- **Spatial Object Detection**: Fine-grained detection in microscopy, pathology slides, and cellular imaging
- **Medical Report Summarization**: Extract and summarize key clinical findings from complex medical reports

### Supported Modalities
- Radiology (X-ray, CT, MRI, Ultrasound)
- Pathology & Microscopy
- Ophthalmology (Fundus, OCT)
- Dermatology
- Nuclear Medicine (PET, SPECT)

## 🚀 Quick Start

### Installation

```bash
pip install transformers torch qwen-vl-utils
```

### Basic Usage

```python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch

# Load model
model = Qwen3VLForConditionalGeneration.from_pretrained(
    "MBZUAI/MedMO-4B",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

processor = AutoProcessor.from_pretrained("MBZUAI/MedMO-4B")

# Prepare your input
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "path/to/medical/image.png",
            },
            {"type": "text", "text": "What abnormalities are present in this chest X-ray?"},
        ],
    }
]

# Process and generate
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)

# Generate output
generated_ids = model.generate(**inputs, max_new_tokens=512)
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[0])
```

### Example: Disease Localization with Bounding Boxes

```python
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "chest_xray.png"},
            {"type": "text", "text": "Detect and localize all abnormalities in this image."},
        ],
    }
]
# Output: "Fractures <box>[[156, 516, 231, 607], [240, 529, 296, 581]]</box>"
```

### Example: Report Generation

```python
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": "ct_scan.png"},
            {"type": "text", "text": "Generate a detailed radiology report for this CT scan."},
        ],
    }
]
# MedMO generates comprehensive clinical reports with findings and impressions
```

## 🏗️ Model Architecture

MedMO is built on **Qwen3-VL-4B-Instruct** and trained through a 4-stage progressive pipeline:

1. **Stage 1 - General Medical SFT**: Large-scale training on 18.5M image-text pairs for foundational medical understanding
2. **Stage 2 - High-Resolution & Grounding**: Training on 3M curated samples at 1280×1280 resolution for spatial localization
3. **Stage 3 - Instruction Tuning**: Fine-tuning on 4.3M instruction-response pairs for task-specific alignment
4. **Stage 4 - Reinforcement Learning**: GRPO training with verifiable rewards (label accuracy, bbox IoU) for enhanced grounding

**Total Training Data**: 26M+ samples from 45 medical datasets spanning diverse modalities and anatomical systems.



For detailed benchmark results, please refer to our paper.

## 📄 Citation

If you use MedMO in your research, please cite our paper:

```bibtex
@article{deria2026medmo,
  title={MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images},
  author={Deria, Ankan and Kumar, Komal and Dukre, Adinath Madhavrao and Segal, Eran and Khan, Salman and Razzak, Imran},
  journal={arXiv preprint arXiv:2602.06965},
  year={2026}
}
```


## 📜 License

This project is licensed under the **Apache License 2.0** - see the [LICENSE](LICENSE) file for details.