Automated Deep Learning-Based Diagnosis of AML Using Flow Cytometry with Olga Pozdnyakova and Joshua Lewis

Flow cytometric analysis of blood & bone marrow for diagnosis of acute myelogenous leukemia (AML) relies heavily on manual intervention in the processing & analysis steps. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions & generate interpretable insights regarding the classification of a sample from individual events.
The Drs. Olga Pozdnyakova and Joshua Lewis discuss their newly developed computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. The study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis & molecular characterization.