RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting
- Imperial College London（帝国理工学院）
- Technische Universität München（慕尼黑工业大学）
- FAU Erlangen–Nürnberg（埃尔朗根-纽伦堡大学）
- National Institutes of Health（美国国立卫生研究院）
机构由 AI 辅助整理，请以论文原文为准。
英文摘要：
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.
