1. Automated Identification of Cell Populations in Flow Cytometry Data with Transformers
- Author
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Wödlinger, Matthias, Reiter, Michael, Weijler, Lisa, Maurer-Granofszky, Margarita, Schumich, Angela, Sajaroff, Elisa O., Groeneveld-Krentz, Stefanie, Rossi, Jorge G., Karawajew, Leonid, Ratei, Richard, and Dworzak, Michael
- Subjects
Quantitative Biology - Quantitative Methods ,Computer Science - Machine Learning - 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, Comment: The article has been published as an open access article in the Journal for Computers in Biology and Medicine: https://doi.org/10.1016/j.compbiomed.2022.105314
- Published
- 2021
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