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Designing a Robust Radiology Report Generation System
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Recent advances in deep learning have enabled researchers to explore tasks at the intersection of computer vision and natural language processing, such as image captioning, visual question answering, visual dialogue, and visual language navigation. Taking inspiration from image captioning, the task of radiology report generation aims at automatically generating radiology reports by having a comprehensive understanding of medical images. However, automatically generating radiology reports from medical images is a challenging task due to the complexity, diversity, and nature of medical images. In this paper, we outline the design of a robust radiology report generation system by integrating different modules and highlighting best practices drawing upon lessons from our past work and also from relevant studies in the literature. We also discuss the impact of integrating different components to form a single integrated system. We believe that these best practices, when implemented, could improve automatic radiology report generation, augment radiologists in decision making, and expedite diagnostic workflow, in turn improve healthcare and save human lives.
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Vision-Language Models for Automated Chest X-ray Interpretation: Leveraging ViT and GPT-2
On the IU-Xray dataset, SWIN-BART outperforms ViT-B16-BART, SWIN-GPT-2, and ViT-B16-GPT-2 on n-gram and embedding-based report metrics.
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