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

TL;DR

This paper evaluates multimodal AI models combining vision transformers and language models to automate and improve the accuracy of radiology report generation from chest X-ray images.

Contribution

It compares different combinations of vision transformers and language models, identifying the most effective model for automated radiology report generation.

Findings

SWIN Transformer-BART outperforms other models in report quality metrics

Multimodal models significantly reduce report generation time

The study demonstrates the potential for AI to assist radiologists

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 at finding 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.