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

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

Maloca et al. implement convolutional neural network (CNN) to automatically segment OCT images obtained from cynomolgus monkeys. The results are compared to annotations generated by human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visuali...

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Bibliographic Details
Main Authors: Peter M. Maloca, Philipp L. Müller, Aaron Y. Lee, Adnan Tufail, Konstantinos Balaskas, Stephanie Niklaus, Pascal Kaiser, Susanne Suter, Javier Zarranz-Ventura, Catherine Egan, Hendrik P. N. Scholl, Tobias K. Schnitzer, Thomas Singer, Pascal W. Hasler, Nora Denk
Format: Article
Language:English
Published: Nature Portfolio 2021-02-01
Series:Communications Biology
Online Access:https://doi.org/10.1038/s42003-021-01697-y

Internet

https://doi.org/10.1038/s42003-021-01697-y

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