整合多模块设计，提升医学影像报告自动生成的鲁棒性
Designing a Robust Radiology Report Generation System
- 融合多个模块构建一体化报告生成系统
- 通过最佳实践提升生成报告的准确性和可靠性
- 适合医疗AI研发与临床辅助诊断系统开发者
深度学习的发展推动了计算机视觉与自然语言处理交叉任务的研究，如图像描述、视觉问答等。受图像描述启发，放射科报告生成旨在通过全面理解医学影像自动生成报告。然而，由于医学影像的复杂性、多样性及专业性，该任务极具挑战。本文系统性地设计了一个鲁棒的放射科报告生成系统，整合不同模块并总结过往研究与文献中的最佳实践。同时探讨了各组件集成对整体性能的影响。我们相信，这些方法可提升自动报告生成效果，辅助放射科医生决策，加速诊断流程，最终改善医疗质量并挽救生命。
原文摘要 · Abstract (English)
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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