Identifying the number of different white blood cells (WBC) in human blood is an established clinical routine, where WBC are labeled with fluorescent markers.
A novel approach based on machine learning will be presented, where WBC are identified label-free, i.e., without any markers.
We developed an open-source workflow that seamlessly connects the recorded images from the instruments with machine learning. The goal is a presentation of the results relevant for clinicians.
This enables fast, cheap and highly accurate identification of WBC, without destroying the cells and leaves marker channels free to answer other biological questions.