MBZUAI / MedMO-4B

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

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

pip install transformers torch qwen-vl-utils

Basic Usage

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

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

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:

@misc{deria2026medmogroundingunderstandingmultimodal,
      title={MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images}, 
      author={Ankan Deria and Komal Kumar and Adinath Madhavrao Dukre and Eran Segal and Salman Khan and Imran Razzak},
      year={2026},
      eprint={2602.06965},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2602.06965}, 
}

📜 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.