--- license: apache-2.0 --- SwinTRG Model # 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)