Layer-Wise Relevance Propagation in Multi-label Neural Networks to Identify COVID-19 Associated Coinfections
Bello,Marilyn Aguilera,Yaumara Nápoles,Gonzalo García,María M. Bello,Rafael Vanhoof,Koen
Bello,Marilyn
Aguilera,Yaumara
Nápoles,Gonzalo
García,María M.
Bello,Rafael
Vanhoof,Koen
Abstract
COVID-19 has been affected worldwide since the end of 2019. Clinical studies have shown that a factor that increases its lethality is the existence of secondary infections. Coinfections associated with the infection SARS-CoV-2 are classified into bacterial infections and fungal infections. A patient may develop one, both, or neither. From a machine learning point of view, this is considered a multi-label classification problem. In this work, we propose a multi-label neural network able to detect such infections in a patient with SARS-CoV-2 and thus provide the medical community with a diagnosis to guide therapy in these patients. However, neural networks are often considered a “black box” model, as their strength in modeling complex interactions, also make their operation almost impossible to explain. Therefore, we propose three adaptations of the Layer-wise Relevance Propagation algorithm to explain multi-label neural networks. The inclusion of this post-hoc interpretability stage made it possible to identify significant input variables in a classifier output.
Description
Publisher Copyright: © 2021, Springer Nature Switzerland AG.
Date
2021
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Publisher
Springer Science and Business Media Deutschland GmbH
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Keywords
Coinfections, COVID-19, Layer-wise Relevance Propagation, Multi-label scenario, Neural networks, SDG 3 - Good Health and Well-being
Citation
Bello, M, Aguilera, Y, Nápoles, G, García, M M, Bello, R & Vanhoof, K 2021, Layer-Wise Relevance Propagation in Multi-label Neural Networks to Identify COVID-19 Associated Coinfections. in Y Hernández Heredia, V Milián Núñez & J Ruiz Shulcloper (eds), Progress in Artificial Intelligence and Pattern Recognition - 7th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13055 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 3-12, 7th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2021, Virtual, Online, 5/10/21. https://doi.org/10.1007/978-3-030-89691-1_1