Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence.

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, w

SUBMITTER: Maloca PM 

PROVIDER: S-EPMC7864998 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature