hmnshudhmn24/mediview-3d-vision

MEDIVIEW-3D

MEDIVIEW-3D converts 2D medical image slices into a 3D reconstruction, localizes anomalous regions, and generates textual explanations describing the detected regions (size, approximate location, and suggested next steps).

Important: This is a research/demo tool and not a medical device. Do not use for clinical decisions.

Quickstart (demo)

1. Install:

pip install -r requirements.txt

2. Generate synthetic phantom slices:

python examples/generate_synthetic_phantom.py

3. Run inference with thresholding and get a mesh + explanation:

python infer_anomaly.py --source examples/synthetic_phantom --method threshold --out demo_mesh_threshold.ply --explain_out explanation.txt

4. (Optional) Train small UNet and run model-based inference:

python train_unet.py --data examples/synthetic_phantom --epochs 3 --out models/unet_demo.pt
python infer_anomaly.py --source examples/synthetic_phantom --method model --model_path models/unet_demo.pt --out demo_mesh_model.ply --explain_out explanation_model.txt

5. Run Streamlit demo:

streamlit run app.py

What you get

• 3D mesh .ply with anomaly regions colored red
• explanation.txt with human-friendly descriptions of detected regions
• Example synthetic phantom (no patient data)
• Small UNet implementation for demo training

Safety & Limitations

• Demo-only; not clinically validated.
• Do not upload identifiable patient data to public repos.
• For real medical use, integrate robust preprocessing and obtain regulatory approvals.