Designing a Robust Radiology Report Generation System
This work addresses the problem of automating radiology report generation for healthcare professionals, but it appears incremental as it builds on existing methods and lessons from past work.
The paper tackles the challenge of automatically generating radiology reports from medical images by designing a robust system that integrates various modules and best practices, aiming to improve report generation, assist radiologists, and enhance healthcare workflows.
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.