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901688c 90157d6 901688c 7012b23 6403294 7012b23 6403294 7012b23 6403294 7012b23 6403294 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | # BiomedCLIP MRI + Clinical Text Classifier
This model fine-tunes [BiomedCLIP (PubMedBERT ViT-B/16)](https://huggingface.co/microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224) for **Alzheimer’s disease classification** from **MRI (3D volumes)** and **synthetic clinical text**.
---
## 🧩 Model Details
- **Backbone**: BiomedCLIP (image + text encoders)
- **Input MRI**: 3D NIfTI → reduced to 3 mid-slices (axial, coronal, sagittal) → stacked into RGB
- **Input Text**: Synthetic patient note (tokenized with PubMedBERT)
- **Fusion**: Concatenate image & text embeddings
- **Head**: MLP (Linear → ReLU → Dropout → Linear) → 3-way classification
- **Labels**:
- `CN` – Cognitively Normal
- `MCI` – Mild Cognitive Impairment
- `Dementia`
---
## 🚀 Usage
### Install
```bash
pip install open_clip_torch nibabel torch torchvision
##Load Pretrained Model
```
import torch
from model import BiomedClipClassifier, predict_from_paths
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load from repo (assuming you pushed pytorch_model.bin + config.json here)
model = BiomedClipClassifier.from_pretrained(".", device=device)
# Example inference
pred, probs = predict_from_paths(
model,
"/path/to/sample_brain.nii.gz",
"Patient shows mild memory impairment and hippocampal atrophy.",
device=device
)
print("Prediction:", pred)
print("Probabilities:", probs) # [CN, MCI, Dementia]
```
##Run Inference
```
python inference.py --weights . --mri /path/to/sample.nii.gz --text "Patient shows memory issues"
```
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