Self-supervised learning for anomaly detection on time series: application to cellular data

Abstract

International audienceThis paper presents a new method for anomaly detec-tion in time series and its application to cellular data.These time series are computed from cell images ac-quired thanks to lens-free microscopy. In the context ofcellular biology, detecting abnormal cells is interestingfor any further analysis. Indeed, cells that deviate fromhealthy trajectories can further drive tissues towarddiseases [RAG+20]. It would be both time-consumingand costly to manually analyse each cell in a dataset often thoudands cells. To overcome this human process,we present a deep self-supervised approach to automat-ically detect abnormal cells from their dry mass timeseries. A 1D-convolutio nal neural network is trained topredict the dry mass of cells. An anomaly is detected ifthe mean squared error (MSE) between prediction andground truth is above a fixed threshold. This processbased on self-supervised learning is tested on a datasetof 9,100 time series of dry mass. The method succeedsin detecting abnormal time series with a precision of 96.6%