A Machine Learning Approach to the Classification of Acute Leukemias and Distinction From Nonneoplastic Cytopenias Using Flow Cytometry Data

Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders

(2021) Ahmad Nanaa et al.   PATHOLOGY

Recent Advances in Computer-Assisted Algorithms for Cell Subtype Identification of Cytometry Data

(2020) Peng Liu et al.   Frontiers in Cell and Developmental Biology

Machine learning and artificial intelligence in haematology

(2020) Roni Shouval et al.   BRITISH JOURNAL OF HAEMATOLOGY

Hematologist‐Level Classification of Mature B‐Cell Neoplasm Using Deep Learning on Multiparameter Flow Cytometry Data

(2020) Max Zhao et al.   CYTOMETRY PART A

Machine learning in haematological malignancies

(2020) Nathan Radakovich et al.   Lancet Haematology

Augmented Human Intelligence and Automated Diagnosis in Flow Cytometry for Hematologic Malignancies

(2020) David P Ng et al.   AMERICAN JOURNAL OF CLINICAL PATHOLOGY

Clinical applications of machine learning algorithms: beyond the black box

(2019) David S Watson et al.   BMJ-British Medical Journal

Automated Flow Cytometric MRD Assessment in Childhood Acute B‐ Lymphoblastic Leukemia Using Supervised Machine Learning

(2019) Michael Reiter et al.   CYTOMETRY PART A

From big flow cytometry datasets to smart diagnostic strategies: The EuroFlow approach

(2019) C.E. Pedreira et al.   JOURNAL OF IMMUNOLOGICAL METHODS

Computational analysis of flow cytometry data in hematological malignancies

(2019) Carolien Duetz et al.   CURRENT OPINION IN ONCOLOGY

Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome

(2018) Bor-Sheng Ko et al.   EBioMedicine

Automated Assessment of Disease Progression in Acute Myeloid Leukemia by Probabilistic Analysis of Flow Cytometry Data

(2017) Bartek Rajwa et al.   IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Depth of Response in Multiple Myeloma: A Pooled Analysis of Three PETHEMA/GEM Clinical Trials

(2017) Juan-Jose Lahuerta et al.   JOURNAL OF CLINICAL ONCOLOGY

Automated database-guided expert-supervised orientation for immunophenotypic diagnosis and classification of acute leukemia

(2017) L Lhermitte et al.   LEUKEMIA

Use of internal control T-cell populations in the flow cytometric evaluation for T-cell neoplasms

(2016) Alicia M. Hunt et al.   CYTOMETRY PART B-CLINICAL CYTOMETRY

Prognostic significance of minimal residual disease in high risk B-ALL: a report from Children's Oncology Group study AALL0232

(2015) M. J. Borowitz et al.   BLOOD

Segmentation and Classification of Bone Marrow Cells Images Using Contextual Information for Medical Diagnosis of Acute Leukemias

(2015) Carolina Reta et al.   PLoS One

OpenCyto: An Open Source Infrastructure for Scalable, Robust, Reproducible, and Automated, End-to-End Flow Cytometry Data Analysis

(2014) Greg Finak et al.   PLoS Computational Biology

Critical assessment of automated flow cytometry data analysis techniques

(2013) Nima Aghaeepour et al.   NATURE METHODS

Combining Mixture Components for Clustering

(2010) Jean-Patrick Baudry et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

Flow cytometric immunophenotyping for hematologic neoplasms

(2008) F. E. Craig et al.   BLOOD