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

2021-07-05 15:58:48
Benjamin Hou, Georgios Kaissis, Ronald Summers, Bernhard Kainz
arXiv_CV
             
Abstract

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: this http URL.

Abstract (translated)

URL

https://arxiv.org/abs/2107.02104

PDF

https://arxiv.org/pdf/2107.02104.pdf

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