Wood, DA;
Kafiabadi, S;
Al Busaidi, A;
Guilhem, E;
Lynch, J;
Townend, M;
Montvila, A;
... Booth, TC; + view all
(2020)
Labelling Imaging Datasets on the Basis of Neuroradiology Reports: A Validation Study.
In:
Interpretable and Annotation-Efficient Learning for Medical Image Computing. IMIMIC 2020, MIL3ID 2020, LABELS 2020.
(pp. pp. 254-265).
Springer: Cham, Switzerland.
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Abstract
Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision applications. To date, however, there has been no thorough investigation into the validity of this approach, including determining the accuracy of report labels compared to image labels as well as examining the performance of non-specialist labellers. In this work, we draw on the experience of a team of neuroradiologists who labelled over 5000 MRI neuroradiology reports as part of a project to build a dedicated deep learning-based neuroradiology report classifier. We show that, in our experience, assigning binary labels (i.e. normal vs abnormal) to images from reports alone is highly accurate. In contrast to the binary labels, however, the accuracy of more granular labelling is dependent on the category, and we highlight reasons for this discrepancy. We also show that downstream model performance is reduced when labelling of training reports is performed by a non-specialist. To allow other researchers to accelerate their research, we make our refined abnormality definitions and labelling rules available, as well as our easy-to-use radiology report labelling tool which helps streamline this process.
| Type: | Proceedings paper |
|---|---|
| Title: | Labelling Imaging Datasets on the Basis of Neuroradiology Reports: A Validation Study |
| ISBN-13: | 9783030611651 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-61166-8_27 |
| Publisher version: | http://dx.doi.org/10.1007/978-3-030-61166-8_27 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Natural language processing, Deep learning, Labelling |
| 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.ucl.ac.uk/id/eprint/10117724 |
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