RATCHET: Medical transformer for chest X-ray diagnosis and reporting
File(s)
Accepted version
OA Location
Author(s)
Hou, Benjamin
Kaissis, Georgios
Summers, Ronald M
Kainz, Bernhard
Type
Conference Paper
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: http://www.github.com/farrell236/RATCHET.
Editor(s)
DeBruijne, M
Cattin, PC
Cotin, S
Padoy, N
Speidel, S
Zheng, Y
Essert, C
Date Issued
2021-09-21
Date Acceptance
2021-06-11
Citation
Lecture Notes in Computer Science, 2021, 12907, pp.293-303
ISBN
978-3-030-87233-5
ISSN
0302-9743
Publisher
Springer
Start Page
293
End Page
303
Journal / Book Title
Lecture Notes in Computer Science
Volume
12907
Copyright Statement
© 2021 Springer Nature Switzerland AG. The final publication is available at Springer via https://link.springer.com/chapter/10.1007/978-3-030-87234-2_28
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000712024400028&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
24th International Conference on Medical Image Computing and Computer Assisted Intervention
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Artificial Intelligence
Computer Science, Software Engineering
Engineering, Biomedical
Medicine, General & Internal
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Engineering
General & Internal Medicine
Publication Status
Published
Start Date
2021-09-27
Finish Date
2021-10-01
Coverage Spatial
ELECTR NETWORK
Date Publish Online
2021-09-21