This review will explore the integration of machine learning (ML) techniques to enhance the
analysis of increasingly complex and voluminous flow cytometry data, as traditional manual …

Background The proportion of residual leukemic blasts after chemotherapy assessed by
multiparameter flow cytometry, is an important prognostic factor for the risk of relapse and …

Abstract Background and Objective: Flow cytometry is a widely used technique for
identifying cell populations in patient-derived fluids, such as peripheral blood (PB) or …

This paper evaluates various deep learning methods for measurable residual disease
(MRD) detection in flow cytometry (FCM) data, addressing questions regarding the benefits …

Acute Leukemia is the most common hematologic malignancy in children and adolescents.
A key methodology in the diagnostic evaluation of this malignancy is immunophenotyping …

This thesis explores the application of Model-Agnostic Meta-Learning (MAML) for classifying
blast cells in Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) …

Dominant neural network architectures such as Convolutional Neural Networks (CNNs) and
Recurrent Neural Networks (RNNs) are inherently local and require deep networks to …