Exploring the potential of stain-free immune cell classification with classical and deep machine learning methods
Identifying cell types with imaging flow cytometry – a high-throughput single-cell imaging technology - requires targeted staining of cell types of interest. However, staining potentially alters the cells under study, and requires manual, expert analysis of measurements to establish cell types. In this work we compare classical and deep machine learning workflows to classify immune cells from human blood in an automated manner using only stain-free images.
Maxim is pursuing a PhD in bio-informatics at Ghent University in the Saeys lab. He obtained his Master degree in Bio-informatics at Ghent University in 2018. In his research he strives to extract biological insights from high-dimensional imaging flow cytometry data by applying and developing machine learning methods.