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Chest radiographs are one of the most common diagnostic modalities inclinical routine. It can be done cheaply, requires minimal equipment, and theimage can be diagnosed by every radiologists. However, the number of chestradiographs obtained on a daily basis can easily overwhelm the availableclinical capacities. We propose RATCHET: RAdiological Text Captioning for HumanExamined Thoraces. RATCHET is a CNN-RNN-based medical transformer that istrained end-to-end. It is capable of extracting image features from chestradiographs, and generates medically accurate text reports that fit seamlesslyinto clinical work flows. The model is evaluated for its natural languagegeneration ability using common metrics from NLP literature, as well as itsmedically accuracy through a surrogate report classification task. The model isavailable for download at: http://www.github.com/farrell236/RATCHET.

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