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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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/05979fb5418e464a9cf6a4f9447c2d75
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spelling	oai:doaj.org-article:05979fb5418e464a9cf6a4f9447c2d752021-12-02T14:06:19ZUnraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence10.1038/s42003-021-01697-y2399-3642https://doaj.org/article/05979fb5418e464a9cf6a4f9447c2d752021-02-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-01697-yhttps://doaj.org/toc/2399-3642Maloca 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 visualized.Peter M. MalocaPhilipp L. MüllerAaron Y. LeeAdnan TufailKonstantinos BalaskasStephanie NiklausPascal KaiserSusanne SuterJavier Zarranz-VenturaCatherine EganHendrik P. N. SchollTobias K. SchnitzerThomas SingerPascal W. HaslerNora DenkNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 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 Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence
description	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 visualized.
format	article
author	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
author_facet	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
author_sort	Peter M. Maloca
title	Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence
title_short	Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence
title_full	Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence
title_fullStr	Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence
title_full_unstemmed	Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence
title_sort	unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/05979fb5418e464a9cf6a4f9447c2d75
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Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

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