Recent developments in the field of Natural Language Processing, especially language models such as the transformer have brought state-of-the-art results in language understanding and language generation. In this work, we investigate the use of the transformer model for radiology report generation from chest X-rays. We also highlight limitations in evaluating radiology report generation using only the standard language generation metrics. We then applied a transformer based radiology report generation architecture, and also compare the performance of a transformer based decoder with the recurrence based decoder. Experiments were performed using the IU-CXR dataset, showing superior results to its LSTM counterpart and being significantly faster. Finally, we identify the need of evaluating radiology report generation system using both language generation metrics and classification metrics, which helps to provide robust measure of generated reports in terms of their coherence and diagnostic value.

本研究解决了放射报告生成过程中的评价局限，提出利用变压器模型从胸部X光片生成放射报告的方法，展现出在生成速度和效果上优于传统LSTM模型的优势。我们强调在评估生成报告时应结合语言生成和分类指标，以确保报告的连贯性和诊断价值。

基于临床背景的医学影像放射报告生成研究