Vision-Language Models for Automated Chest X-ray Interpretation: Leveraging ViT and GPT-2

Abstract

Radiology plays a pivotal role in modern medicine due to its non-invasive diagnostic capabilities. However, the manual generation of unstructured medical reports is time-consuming and prone to errors. It creates a significant bottleneck in clinical workflows. Despite advancements in AI-generated radiology reports, challenges remain in achieving detailed and accurate report generation. In this study, we have evaluated different combinations of multimodal models that integrate Computer Vision and Natural Language Processing to generate comprehensive radiology reports. We employed a pretrained Vision Transformer (ViT-B16) and a SWIN Transformer as the image encoders. The BART and GPT-2 models serve as the textual decoders. We used Chest X-ray images and reports from the IU-Xray dataset to evaluate the usability of the SWIN Transformer-BART, SWIN Transformer-GPT-2, ViT-B16-BART, and ViT-B16-GPT-2 models for report generation. We aimed to find the best combination among the models. The SWIN-BART model performs as the best-performing model among the four models, achieving remarkable results in almost all the evaluation metrics like ROUGE, BLEU, and BERTScore.