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
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.
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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