StArDusTS is a self-supervised anomaly detection model that can accurately detect anomalies in cellular dry mass time series data without any human priors. It has been tested on different cell lines and has shown a precision of 96%. The model can also detect measurement errors in the acquisition and analysis pipelines, leading to potential improvements in cell imaging and analysis methods. The model was developed using lens-free microscopy and machine learning techniques.
