🩺 MedFusion-AI (Unified Radiology Pipeline)
Pro + Lite unified radiology model — powered by MedSigLIP & MedGemma
🧠 About the Model
MedFusion-AI is a unified multimodal medical-AI pipeline integrating vision encoders and text decoders to produce full radiology reports from X-ray or DICOM inputs.
| Mode | Encoder | Decoder | Precision |
|---|---|---|---|
| Pro | fokan/medsiglip-448-fp16-pruned20 |
fokan/medgemma-4b-it-fp16-pruned20 |
FP16 + Pruned (High accuracy) |
| Lite | fokan/medsiglip-448-int8 |
fokan/medgemma-4b-it-int8 |
INT8 (Compact & fast) |
🩻 Usage (Python)
from medfusion_pipeline import MedFusionPipeline
pipe = MedFusionPipeline.from_pretrained(".", mode="pro") # or 'lite'
report = pipe.analyze("sample_xray.jpg")
print(report)
⚙️ Modes
- pro → FP16 + Pruned (High accuracy)
- lite → INT8 (Compact speed-optimized)
💡 Features
- Handles X-ray / DICOM inputs automatically
- Generates structured radiology reports
- Plug-and-play dual pipeline (Pro & Lite)
- Optimized for medical education + research
🧩 Deployment Options
| Platform | Description |
|---|---|
| 🤗 Hugging Face Spaces | One-click Gradio demo or inference API |
| 🚀 HF Inference Endpoint | GPU-backed endpoint for production |
| 💻 Local Deployment | Python + Torch runtime (CPU/GPU friendly) |
📊 Model Specs
- Architecture: MedSigLIP encoder + MedGemma decoder
- Params: ~4 B (Teacher) → ~0.4 B (Student Distilled)
- Input Resolution: 224 / 448 px
- Optimized for: Chest X-rays & general radiographs
📜 Citation
If you use MedFusion-AI in research, please cite:
@software{fokan_medfusion_ai_2025,
title={MedFusion-AI: Unified Radiology Encoder-Decoder Pipeline},
author={Karrar Alhdrawi},
year={2025},
url={https://huggingface.co/fokan/MedFusion-AI}
}
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