Comparison of three machine learning algorithms for classification of B‐cell neoplasms using clinical flow cytometry data

Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the i...

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Veröffentlicht in:	Cytometry. Part B, Clinical cytometry Jg. 106; H. 4; S. 282 - 293
Hauptverfasser:	Dinalankara, Wikum, Ng, David P., Marchionni, Luigi, Simonson, Paul D.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 01.07.2024 Wiley Subscription Services, Inc
Schlagworte:	Accuracy Algorithms Artificial intelligence B-Lymphocytes - classification B-Lymphocytes - immunology B-Lymphocytes - pathology B‐cell neoplasms Classification deep learning Flow cytometry Flow Cytometry - methods Humans Immunophenotyping - methods Learning algorithms Lymphoma, B-Cell - classification Lymphoma, B-Cell - diagnosis Lymphoma, B-Cell - pathology Machine Learning Neoplasms B‐cell neoplasms deep learning flow cytometry machine learning
ISSN:	1552-4949, 1552-4957, 1552-4957
Online-Zugang:	Volltext
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