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Code-free deep learning for multi-modality medical image classification

Korot, E; Guan, Z; Ferraz, D; Wagner, SK; Zhang, G; Liu, X; Faes, L; ... Keane, PA; + view all (2021) Code-free deep learning for multi-modality medical image classification. Nature Machine Intelligence 10.1038/s42256-021-00305-2. (In press). Green open access

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Abstract

© 2021, The Author(s). A number of large technology companies have created code-free cloud-based platforms that allow researchers and clinicians without coding experience to create deep learning algorithms. In this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models. The mean (s.d.) F1 scores across platforms for all model–dataset pairs were as follows: Amazon, 93.9 (5.4); Apple, 72.0 (13.6); Clarifai, 74.2 (7.1); Google, 92.0 (5.4); MedicMind, 90.7 (9.6); Microsoft, 88.6 (5.3). The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. Potential use cases given proper validation include research dataset curation, mobile ‘edge models’ for regions without internet access, and baseline models against which to compare and iterate bespoke deep learning approaches.

Type: Article
Title: Code-free deep learning for multi-modality medical image classification
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/s42256-021-00305-2
Publisher version: https://doi.org/10.1038/s42256-021-00305-2
Language: English
Additional information: © 2021 Springer Nature Limited. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Keywords: Diagnostic markers; Eye manifestations; Mathematics and computing; Medical imaging; Science, technology and society
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
URI: https://discovery.ucl.ac.uk/id/eprint/10124083
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