Group 22: Machine Learning in Leukemia Diagnosis

Leukemia is one of the major types of cancer in the United States. The current diagnosis of leukemia based on flow cytometry data, a process called manual gating, can be very subjective, time-consuming, and labor-intensive. We believe that computers can outperform humans in identifying rare cell populations and providing tumor burden estimation. In this project, we will assess different computational methods in their ability in identifying blood cancer cells.