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

Hou B, Kaissis G, Summers R, Kainz B (2021)

Publication Status: Published

Publication Type: Authored book, Volume of book series

Publication year: 2021

Publisher: Springer Science and Business Media Deutschland GmbH

Pages Range: 293-303

ISBN: 9783030872335

DOI: 10.1007/978-3-030-87234-2_28

Open Access Link: https://arxiv.org/abs/2107.02104

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.

Authors with CRIS profile

Bernhard Kainz Professur für Image Data Exploration and Analysis

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) US National Institutes of Health (NIH) US United States (USA) (US)

How to cite

APA:

Hou, B., Kaissis, G., Summers, R., & Kainz, B. (2021). RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting. Springer Science and Business Media Deutschland GmbH.

MLA:

Hou, Benjamin, et al. RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting. Springer Science and Business Media Deutschland GmbH, 2021.

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