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

Abstract

International audienceWe 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%