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Nuances of interpreting X-ray analysis by deep learning and lessons for reporting experimental findings

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Date
16/01/2022
Author
Valsson, Steinar
Arandjelović, Ognjen
Keywords
Roentgen
Chest
Disease
Thorax
Error
Label
QA76 Computer software
RC Internal medicine
DAS
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Abstract
With the increase in the availability of annotated X-ray image data, there has been an accompanying and consequent increase in research on machine-learning-based, and ion particular deep-learning-based, X-ray image analysis. A major problem with this body of work lies in how newly proposed algorithms are evaluated. Usually, comparative analysis is reduced to the presentation of a single metric, often the area under the receiver operating characteristic curve (AUROC), which does not provide much clinical value or insight and thus fails to communicate the applicability of proposed models. In the present paper, we address this limitation of previous work by presenting a thorough analysis of a state-of-the-art learning approach and hence illuminate various weaknesses of similar algorithms in the literature, which have not yet been fully acknowledged and appreciated. Our analysis was performed on the ChestX-ray14 dataset, which has 14 lung disease labels and metainfo such as patient age, gender, and the relative X-ray direction. We examined the diagnostic significance of different metrics used in the literature including those proposed by the International Medical Device Regulators Forum, and present the qualitative assessment of the spatial information learned by the model. We show that models that have very similar AUROCs can exhibit widely differing clinical applicability. As a result, our work demonstrates the importance of detailed reporting and analysis of the performance of machine-learning approaches in this field, which is crucial both for progress in the field and the adoption of such models in practice.
Citation
Valsson , S & Arandjelović , O 2022 , ' Nuances of interpreting X-ray analysis by deep learning and lessons for reporting experimental findings ' , Sci , vol. 4 , no. 1 , 3 . https://doi.org/10.3390/sci4010003
Publication
Sci
Status
Peer reviewed
DOI
https://doi.org/10.3390/sci4010003
ISSN
2413-4155
Type
Journal article
Rights
Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/24693

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