Romain Bailly, Marielle Malfante, Cédric Allier, Chiara Paviolo, Lamya Ghenim, Kiran Padmanabhan, Sabine Bardin, Jérôme Mars
The prediction of pathological changes on single cell behaviour is a challenging task for deep learning models. Indeed, in self-supervised learning methods, no prior labels are used for the training and all of the information for event predictions are extracted from the data themselves. We present here a novel self-supervised learning model for the detection of anomalies in a given cell population, StArDusTS. Cells are monitored over time, and analysed to extract time-series of dry mass values. We assessed its performances on different cell lines, showing a precision of 96% in the automatic detection of anomalies...
March 25, 2024: Scientific Reports