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RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting

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arxiv 2107.02104 v2 pith:OPEV7GJ4 submitted 2021-07-05 cs.CV

classification cs.CV
keywords chestratchetclinicalradiographsavailablecommonimagemedical
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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