Automated Identification of Cell Populations in Flow Cytometry Data with Transformers
Matthias Wödlinger, Michael Reiter, Lisa Weijler, Margarita Maurer-Granofszky, Angela Schumich, Elisa O. Sajaroff, Stefanie Groeneveld-Krentz, Jorge G. Rossi, Leonid Karawajew, Richard Ratei, Michael Dworzak
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- github.com/mwoedlinger/flowformerOfficialIn paperpytorch★ 15
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