Journal
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VII
Volume 12907, Issue -, Pages 293-303Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-87234-2_28
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Funding
- UK Research and Innovation London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare
- Intramural Research Program of the National Institutes of Health Clinical Center
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Chest radiographs are commonly used in clinical routine, but may overwhelm clinical capacities. RATCHET is a CNN-RNN-based model that can generate medically accurate text reports, suitable for clinical workflows.
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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