Benger, Matthew;
Wood, David A;
Kafiabadi, Sina;
Al Busaidi, Aisha;
Guilhem, Emily;
Lynch, Jeremy;
Townend, Matthew;
... Booth, Thomas C; + view all
(2023)
Factors affecting the labelling accuracy of brain MRI studies relevant for deep learning abnormality detection.
Frontiers in Radiology
, 3
, Article 1251825. 10.3389/fradi.2023.1251825.
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Abstract
Unlocking the vast potential of deep learning-based computer vision classification systems necessitates large data sets for model training. Natural Language Processing (NLP)—involving automation of dataset labelling—represents a potential avenue to achieve this. However, many aspects of NLP for dataset labelling remain unvalidated. Expert radiologists manually labelled over 5,000 MRI head reports in order to develop a deep learning-based neuroradiology NLP report classifier. Our results demonstrate that binary labels (normal vs. abnormal) showed high rates of accuracy, even when only two MRI sequences (T2-weighted and those based on diffusion weighted imaging) were employed as opposed to all sequences in an examination. Meanwhile, the accuracy of more specific labelling for multiple disease categories was variable and dependent on the category. Finally, resultant model performance was shown to be dependent on the expertise of the original labeller, with worse performance seen with non-expert vs. expert labellers.
| Type: | Article |
|---|---|
| Title: | Factors affecting the labelling accuracy of brain MRI studies relevant for deep learning abnormality detection |
| Location: | Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fradi.2023.1251825 |
| Publisher version: | http://dx.doi.org/10.3389/fradi.2023.1251825 |
| Language: | English |
| Additional information: | Copyright © 2023 Benger, Wood, Kafiabadi, Al Busaidi, Guilhem, Lynch, Townend, Montvila, Siddiqui, Gadapa, Barker, Ourselin, Cole and Booth. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | Deep learning, computer vision system, labelling, neuroradiology, MRI |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10184199 |
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