Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Frick, Thomas | - |
dc.contributor.author | Glüge, Stefan | - |
dc.contributor.author | Rahimi, Abbas | - |
dc.contributor.author | Benini, Luca | - |
dc.contributor.author | Brunschwiler, Thomas | - |
dc.date.accessioned | 2021-03-14T11:17:53Z | - |
dc.date.available | 2021-03-14T11:17:53Z | - |
dc.date.issued | 2021-02-21 | - |
dc.identifier.isbn | 978-3-030-70568-8 | de_CH |
dc.identifier.isbn | 978-3-030-70569-5 | de_CH |
dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/21995 | - |
dc.description.abstract | Neural Networks are powerful classifiers. However, they are black boxes and do not provide explicit explanations for their decisions. For many applications, particularly in health care, explanations are essential for building trust in the model. In the field of computer vision, a multitude of explainability methods have been developed to analyze Neural Networks by explaining what they have learned during training and what factors influence their decisions. This work provides an overview of these explanation methods in form of a taxonomy. We adapt and benchmark the different methods to time series data. Further, we introduce quantitative explanation metrics that enable us to build an objective benchmarking framework with which we extensively rate and compare explainability methods. As a result, we show that the Grad-CAM++ algorithm outperforms all other methods. Finally, we identify the limits of existing explanation methods for specific datasets, with feature values close to zero. | de_CH |
dc.language.iso | en | de_CH |
dc.publisher | Springer | de_CH |
dc.relation.ispartofseries | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering | de_CH |
dc.rights | Licence according to publishing contract | de_CH |
dc.subject | Explainable deep learning | de_CH |
dc.subject | Convolutional neural network | de_CH |
dc.subject | Explanation quality metric | de_CH |
dc.subject | Medical time series data | de_CH |
dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
dc.subject.ddc | 362: Gesundheits- und Sozialdienste | de_CH |
dc.title | Explainable deep learning for medical time series data | de_CH |
dc.type | Konferenz: Paper | de_CH |
dcterms.type | Text | de_CH |
zhaw.departement | Life Sciences und Facility Management | de_CH |
zhaw.organisationalunit | Institut für Computational Life Sciences (ICLS) | de_CH |
zhaw.publisher.place | Cham | de_CH |
dc.identifier.doi | 10.1007/978-3-030-70569-5_15 | de_CH |
zhaw.conference.details | International Conference on Wireless Mobile Communication and Healthcare (MobiHealth), Online, 18 December 2020 | de_CH |
zhaw.funding.eu | No | de_CH |
zhaw.originated.zhaw | Yes | de_CH |
zhaw.pages.end | 256 | de_CH |
zhaw.pages.start | 244 | de_CH |
zhaw.publication.status | publishedVersion | de_CH |
zhaw.series.number | 362 | de_CH |
zhaw.publication.review | Peer review (Publikation) | de_CH |
zhaw.title.proceedings | Wireless Mobile Communication and Healthcare | de_CH |
zhaw.webfeed | Predictive Analytics | de_CH |
zhaw.author.additional | No | de_CH |
zhaw.display.portrait | Yes | de_CH |
Appears in collections: | Publikationen Life Sciences und Facility Management |
Files in This Item:
There are no files associated with this item.
Show simple item record
Frick, T., Glüge, S., Rahimi, A., Benini, L., & Brunschwiler, T. (2021). Explainable deep learning for medical time series data [Conference paper]. Wireless Mobile Communication and Healthcare, 244–256. https://doi.org/10.1007/978-3-030-70569-5_15
Frick, T. et al. (2021) ‘Explainable deep learning for medical time series data’, in Wireless Mobile Communication and Healthcare. Cham: Springer, pp. 244–256. Available at: https://doi.org/10.1007/978-3-030-70569-5_15.
T. Frick, S. Glüge, A. Rahimi, L. Benini, and T. Brunschwiler, “Explainable deep learning for medical time series data,” in Wireless Mobile Communication and Healthcare, Feb. 2021, pp. 244–256. doi: 10.1007/978-3-030-70569-5_15.
FRICK, Thomas, Stefan GLÜGE, Abbas RAHIMI, Luca BENINI und Thomas BRUNSCHWILER, 2021. Explainable deep learning for medical time series data. In: Wireless Mobile Communication and Healthcare. Conference paper. Cham: Springer. 21 Februar 2021. S. 244–256. ISBN 978-3-030-70568-8
Frick, Thomas, Stefan Glüge, Abbas Rahimi, Luca Benini, and Thomas Brunschwiler. 2021. “Explainable Deep Learning for Medical Time Series Data.” Conference paper. In Wireless Mobile Communication and Healthcare, 244–56. Cham: Springer. https://doi.org/10.1007/978-3-030-70569-5_15.
Frick, Thomas, et al. “Explainable Deep Learning for Medical Time Series Data.” Wireless Mobile Communication and Healthcare, Springer, 2021, pp. 244–56, https://doi.org/10.1007/978-3-030-70569-5_15.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.