Explainable deep learning for medical time series data

Frick, Thomas; Glüge, Stefan; Rahimi, Abbas; Benini, Luca; Brunschwiler, Thomas

doi:10.1007/978-3-030-70569-5_15

Publication type:	Conference paper
Type of review:	Peer review (publication)
Title:	Explainable deep learning for medical time series data
Authors:	Frick, Thomas Glüge, Stefan Rahimi, Abbas Benini, Luca Brunschwiler, Thomas
et. al:	No
DOI:	10.1007/978-3-030-70569-5_15
Proceedings:	Wireless Mobile Communication and Healthcare
Page(s):	244
Pages to:	256
Conference details:	International Conference on Wireless Mobile Communication and Healthcare (MobiHealth), Online, 18 December 2020
Issue Date:	21-Feb-2021
Series:	Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Series volume:	362
Publisher / Ed. Institution:	Springer
Publisher / Ed. Institution:	Cham
ISBN:	978-3-030-70568-8 978-3-030-70569-5
Language:	English
Subjects:	Explainable deep learning; Convolutional neural network; Explanation quality metric; Medical time series data
Subject (DDC):	006: Special computer methods 362: Health and social services
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.
URI:	https://digitalcollection.zhaw.ch/handle/11475/21995
Fulltext version:	Published version
License (according to publishing contract):	Licence according to publishing contract
Departement:	Life Sciences and Facility Management
Organisational Unit:	Institute of Computational Life Sciences (ICLS)
Appears in collections:	Publikationen Life Sciences und Facility Management

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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.