  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
