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Communication Dans Un Congrès Année : 2021

Deep anomaly detection using self-supervised learning: application to time series of cellular data

Résumé

We present a deep self-supervised method for anomaly detection on time series. We apply this methodology to detect anomalies from cellular time series. In particular, this study focuses on cell dry mass, obtained in the context of lensfree microscopy. The method we propose is an innovative two-step pipeline using self-supervised learning. As a first step, a representation of the time series is learned thanks to a 1D-convolutional neural network without any labels. Then, the learned representation is used to feed a threshold anomaly detector. This new self-supervised learning method is tested on an unlabelled dataset of 9100 time series of dry mass and succeeded in detecting abnormal time series with a precision of 96.6%.
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Dates et versions

cea-03605065 , version 1 (24-11-2021)
cea-03605065 , version 2 (10-03-2022)

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  • HAL Id : cea-03605065 , version 1

Citer

Romain Bailly, Marielle Malfante, Cédric Allier, Lamya Ghenim, Jérôme I. Mars. Deep anomaly detection using self-supervised learning: application to time series of cellular data. ASPAI 2021 - 3rd International Conference on Advances in Signal Processing and Artificial Intelligence, Nov 2021, Porto, Portugal. ⟨cea-03605065v1⟩
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