---
language: en
pipeline_tag: text-classification
tags:
- biogpt
- medical
- radiology
- birads
- classification
license: mit
metrics:
- accuracy
- f1
model-index:
- name: BioGPT BI-RADS Classifier
  results:
  - task:
      type: text-classification
      name: BI-RADS Classification
    metrics:
    - type: accuracy
      value: 0.9736
      name: Accuracy
    - type: f1
      value: 0.9066
      name: F1 (Macro)
---

# BioGPT BI-RADS Classifier

This model is a fine-tuned version of [microsoft/biogpt-large](https://huggingface.co/microsoft/biogpt-large) for BI-RADS classification of radiology reports.

## Model Description

- **Base Model:** microsoft/biogpt-large
- **Task:** Multi-class text classification (BI-RADS categories 0-6)
- **Training Data:** Radiology reports with BI-RADS annotations
- **Accuracy:** 97.36%
- **F1-Score (Macro):** 90.66%

## Performance

### Overall Metrics
- **Accuracy:** 97.36%
- **F1-Score (Macro):** 90.66%
- **F1-Score (Weighted):** 97.34%
- **Precision (Macro):** 92.65%
- **Recall (Macro):** 88.96%

### Per-Class Performance
| BI-RADS | Precision | Recall | F1-Score | Support |
|---------|-----------|--------|----------|---------|
| 0       | 0.9946    | 0.9482 | 0.9708   | 193     |
| 1       | 0.9504    | 0.9664 | 0.9583   | 119     |
| 2       | 0.9740    | 0.9943 | 0.9840   | 527     |
| 3       | 1.0000    | 0.8333 | 0.9091   | 18      |
| 4       | 0.9000    | 0.8182 | 0.8571   | 11      |
| 5       | 0.6667    | 0.6667 | 0.6667   | 3       |
| 6       | 1.0000    | 1.0000 | 1.0000   | 1       |

## Usage

```python
from transformers import AutoTokenizer, BioGptForSequenceClassification
import torch

# Load model and tokenizer
model = BioGptForSequenceClassification.from_pretrained("ishro/biogpt-aura")
tokenizer = AutoTokenizer.from_pretrained("ishro/biogpt-aura")

# Prepare input
report_text = "Your radiology report text here..."
inputs = tokenizer(report_text, return_tensors="pt", padding=True, truncation=True, max_length=512)

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1).item()

# Map to BI-RADS label
birads_label = model.config.id2label[predicted_class]
print(f"Predicted BI-RADS: {birads_label}")
print(f"Confidence: {predictions[0][predicted_class].item():.4f}")
```

## Training Details

### Training Hyperparameters
- **Learning Rate:** 2e-5
- **Batch Size:** 4 per device (2 GPUs)
- **Gradient Accumulation Steps:** 8
- **Effective Batch Size:** 64
- **Epochs:** 3
- **Optimizer:** AdamW (fused)
- **Mixed Precision:** BF16
- **Hardware:** 2x NVIDIA L40S (46GB each)

### Training Data
The model was trained on radiology reports with the following features:
- Report observations
- Conclusions
- Recommendations
- Patient metadata (age, hormonal therapy, family history, etc.)

## Limitations
- Performance on BI-RADS categories 5 and 6 is lower due to limited training samples
- Model is trained on specific radiology report format
- May not generalize well to reports from different institutions without fine-tuning

## Ethical Considerations
- This model is intended for research purposes and should not be used as the sole basis for clinical decisions
- Always consult with qualified medical professionals for diagnosis and treatment
- The model may have biases based on the training data distribution

## Citation
If you use this model, please cite:
```bibtex
@misc{biogpt-birads-classifier,
  author = {Your Name},
  title = {BioGPT BI-RADS Classifier},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/ishro/biogpt-aura}
}
```

## Model Card Authors
ishro

## Model Card Contact
For questions or issues, please open an issue on the model repository.