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.

本研究针对自动生成放射学报告所面临的复杂性和多样性问题，提出了一种稳健的报告生成系统设计方法。通过整合不同模块并借鉴以往研究的经验和文献中的最佳实践，研究结果表明，该系统可以提高自动报告生成的效果，帮助放射科医生做出决策，加速诊断流程，从而改善医疗服务，挽救生命。

设计一个稳健的放射学报告生成系统