|
--- |
|
license: apache-2.0 |
|
--- |
|
|
|
<img src="https://cdn-uploads.huggingface.co/production/uploads/664d875aafe6e8c3a97dccbb/iR0Pmxo6BaZVPzlwtqiqK.webp" alt="SwinTRG Model" width="600"> |
|
|
|
# SwinTRG |
|
SwinTRG is an innovative transformer-based model developed to automate the generation of radiology reports. It combines the Swin Vision Transformer (Swin-ViT) for feature extraction and BioBERT for report generation, providing a powerful tool for improving the efficiency and accuracy of radiology workflows. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
|
|
SwinTRG represents a significant advancement in automating medical report generation, leveraging cutting-edge machine learning technologies to enhance diagnostic processes in radiology. By integrating Swin-ViT for efficient feature extraction from medical images and BioBERT for generating clinically relevant reports, SwinTRG achieves superior performance across key evaluation metrics. |
|
|
|
- **Developed by:** Siyahul Haque T P |
|
- **Shared by:** Siyahul Haque T P |
|
- **Model type:** Hybrid Transformer – Swin Vision Transformer (Swin-ViT) for feature extraction and BioBERT for report generation. |
|
- **Language(s) (NLP):** English (Medical Reports) |
|
- **License:** Apache-2.0 |
|
- **Finetuned from model:** Swin Transformer, BioBERT |
|
|
|
|
|
## Uses |
|
|
|
### Direct Use |
|
|
|
SwinTRG is designed for the automated generation of radiology reports and can be used in: |
|
- Generating detailed and accurate radiology reports from chest X-rays. |
|
- Supporting clinical decision-making by providing structured and clinically relevant information. |
|
- Assisting radiologists in reducing reporting time and improving diagnostic consistency. |
|
|
|
### Downstream Use |
|
|
|
By fine-tuning SwinTRG on specific datasets, it can be adapted to: |
|
- Generate reports for other imaging modalities, such as CT scans or MRIs. |
|
- Integrate with hospital systems for end-to-end automation in diagnostic pipelines. |
|
|
|
### Out-of-Scope Use |
|
|
|
The model is not recommended for: |
|
- Use in domains unrelated to radiology. |
|
- Real-time diagnostics without proper validation in clinical settings. |
|
|
|
## Bias, Risks, and Limitations |
|
|
|
While SwinTRG showcases excellent performance, certain limitations must be considered: |
|
- **Bias Risks:** The model's effectiveness may vary depending on the diversity and quality of the training data. |
|
- **Limitations:** It may struggle with edge cases or images that significantly deviate from the training dataset (e.g., poor-quality scans or rare conditions). |
|
|
|
### Recommendations |
|
|
|
- Deploy SwinTRG with rigorous validation to ensure accuracy in clinical settings. |
|
- Continuously update and fine-tune the model using diverse and representative datasets to address potential biases. |
|
|
|
## How to Get Started with the Model |
|
|
|
Use the following code snippet to begin working with SwinTRG: |
|
|
|
```python |
|
from transformers import AutoModel, AutoProcessor |
|
|
|
# Load the SwinTRG model and processor |
|
model = AutoModel.from_pretrained("siyah1/SwinTRG") |
|
processor = AutoProcessor.from_pretrained("siyah1/SwinTRG") |
|
|
|
# Example input: chest X-ray image |
|
inputs = processor(image, return_tensors="pt") |
|
outputs = model(**inputs) |
|
|
