license: apache-2.0
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:
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)