RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting
This addresses the bottleneck in clinical workflows for radiologists by automating report generation, though it appears incremental as it builds on existing transformer and CNN-RNN methods.
The paper tackles the problem of overwhelming numbers of chest radiographs by proposing RATCHET, a CNN-RNN-based medical transformer that generates medically accurate text reports from chest X-rays, achieving evaluation through NLP metrics and a surrogate classification task.
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.