A quantitative evaluation of deep learning techniques for unsupervised and supervised analysis of high-dimensional cytometry data Ghent University
Recent advances in cytometry allow scientists to measure many parameters at a single-cell resolution, and this for millions of cells across tens to hundreds of patients, possibly at different time points. To make sense of all this data, novel machine learning approaches for visualisation, automated population identification and subsequent differential analysis between groups of patients will be explored and compared. In this project in ...