Automated identification of cell populations in flow cytometry data with transformers
Matthias Wödlinger, Michael J. Reiter, Lisa Weijler, Margarita Maurer‐Granofszky, Angela Schumich, Elisa O. Sajaroff, Stefanie Groeneveld‐Krentz, Jorge G. Rossi, Leonid Karawajew, Richard Ratei, Michael Dworzak
Abstract
Acute Lymphoblastic Leukemia (ALL) is the most frequent hematologic malignancy in children and adolescents. A strong prognostic factor in ALL is given by the Minimal Residual Disease (MRD), which is a measure for the number of leukemic cells persistent in a patient. Manual MRD assessment from Multiparameter Flow Cytometry (FCM) data after treatment is time-consuming and subjective. In this work, we present an automated method to compute the MRD value directly from FCM data. We present a novel neural network approach based on the transformer architecture that learns to directly identify blast cells in a sample. We train our method in a supervised manner and evaluate it on publicly available ALL FCM data from three different clinical centers. Our method reaches a median F1 score of ≈0.94 when evaluated on 519 B-ALL samples and shows better results than existing methods on 4 different datasets.