Maloca, Peter M;
Mueller, Philipp L;
Lee, Aaron Y;
Tufail, Adnan;
Balaskas, Konstantinos;
Niklaus, Stephanie;
Kaiser, Pascal;
... Denk, Nora; + view all
(2021)
Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence.
Communications Biology
, 4
(1)
, Article 170. 10.1038/s42003-021-01697-y.
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Abstract
Machine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization (‘neural recording’). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications.
| Type: | Article |
|---|---|
| Title: | Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s42003-021-01697-y |
| Publisher version: | https://doi.org/10.1038/s42003-021-01697-y |
| Language: | English |
| Additional information: | © 2023 Springer Nature Limited. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| 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/10165121 |
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