Chest radiographs are one of the most common diagnostic modalities in clinical routine. It can be done cheaply, requires minimal equipment, and the image can be diagnosed by every radiologists. However, the number of chest radiographs obtained on a daily basis can easily overwhelm the available clinical capacities. We propose RATCHET: RAdiological Text Captioning for Human Examined Thoraces. RATCHET is a CNN-RNN-based medical transformer that is trained end-to-end. It is capable of extracting image features from chest radiographs, and generates medically accurate text reports that fit seamlessly into clinical work flows. The model is evaluated for its natural language generation ability using common metrics from NLP literature, as well as its medically accuracy through a surrogate report classification task. The model is available for download at: http://www.github.com/farrell236/RATCHET.
翻译:切片是临床常规中最常见的诊断方式之一,可以廉价地进行,需要最起码的设备,图像可以由每个放射科医生诊断。然而,每天获得的胸腔射线可以很容易地压倒现有的临床能力。我们提议RATCHET:人类检查的血清文字说明。RATCHET是CNN-RNNN的医学变压器,其端对端经过训练。它能够从胸腔射线中提取图像特征,并生成与临床工作流完全吻合的医学准确的文本报告。该模型使用NLP文献的通用指标评估其自然语言生成能力,并通过代用报告分类任务评估其医学准确性。该模型可在以下网址下载:http://www.github.com/farrell236/RATCHET。