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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21902
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dc.contributor.authorMaloca, Peter M.-
dc.contributor.authorMüller, Philipp L.-
dc.contributor.authorLee, Aaron Y.-
dc.contributor.authorTufail, Adnan-
dc.contributor.authorBalaskas, Konstantinos-
dc.contributor.authorNiklaus, Stephanie-
dc.contributor.authorKaiser, Pascal-
dc.contributor.authorSuter, Susanne-
dc.contributor.authorZarranz-Ventura, Javier-
dc.contributor.authorEgan, Catherine-
dc.contributor.authorScholl, Hendrik P. N.-
dc.contributor.authorSchnitzer, Tobias K.-
dc.contributor.authorSinger, Thomas-
dc.contributor.authorHasler, Pascal W.-
dc.contributor.authorDenk, Nora-
dc.date.accessioned2021-03-04T07:55:35Z-
dc.date.available2021-03-04T07:55:35Z-
dc.date.issued2021-02-05-
dc.identifier.issn2399-3642de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/21902-
dc.description.abstractMachine 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.de_CH
dc.language.isoende_CH
dc.publisherNature Publishing Groupde_CH
dc.relation.ispartofCommunications Biologyde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectMedical researchde_CH
dc.subjectMolecular medicinede_CH
dc.subjectDeep learningde_CH
dc.subjectExplainable AIde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleUnraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligencede_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.1038/s42003-021-01697-yde_CH
dc.identifier.doi10.21256/zhaw-21902-
dc.identifier.pmid33547415de_CH
zhaw.funding.euNode_CH
zhaw.issue1de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start170de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume4de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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Maloca, P. M., Müller, P. L., Lee, A. Y., Tufail, A., Balaskas, K., Niklaus, S., Kaiser, P., Suter, S., Zarranz-Ventura, J., Egan, C., Scholl, H. P. N., Schnitzer, T. K., Singer, T., Hasler, P. W., & Denk, N. (2021). Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence. Communications Biology, 4(1), 170. https://doi.org/10.1038/s42003-021-01697-y
Maloca, P.M. et al. (2021) ‘Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence’, Communications Biology, 4(1), p. 170. Available at: https://doi.org/10.1038/s42003-021-01697-y.
P. M. Maloca et al., “Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence,” Communications Biology, vol. 4, no. 1, p. 170, Feb. 2021, doi: 10.1038/s42003-021-01697-y.
MALOCA, Peter M., 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 und Nora DENK, 2021. Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence. Communications Biology. 5 Februar 2021. Bd. 4, Nr. 1, S. 170. DOI 10.1038/s42003-021-01697-y
Maloca, Peter M., Philipp L. Müller, Aaron Y. Lee, Adnan Tufail, Konstantinos Balaskas, Stephanie Niklaus, Pascal Kaiser, et al. 2021. “Unraveling the Deep Learning Gearbox in Optical Coherence Tomography Image Segmentation towards Explainable Artificial Intelligence.” Communications Biology 4 (1): 170. https://doi.org/10.1038/s42003-021-01697-y.
Maloca, Peter M., et al. “Unraveling the Deep Learning Gearbox in Optical Coherence Tomography Image Segmentation towards Explainable Artificial Intelligence.” Communications Biology, vol. 4, no. 1, Feb. 2021, p. 170, https://doi.org/10.1038/s42003-021-01697-y.


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