Open AccessDe Novo Identification and Visualization of Important Cell Populations for Classic Hodgkin Lymphoma Using Flow Cytometry and Machine Learning
Author(s) -
Paul D. Simonson,
Yue Wu,
David Wu,
Jonathan R. Fromm,
Aaron Lee
Publication year - 2021
Publication title -
american journal of clinical pathology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.859
H-Index - 128
eISSN - 1943-7722
pISSN - 0002-9173
DOI - 10.1093/ajcp/aqab076
Subject(s) - flow cytometry , histogram , computer science , visualization , artificial intelligence , pattern recognition (psychology) , cytometry , convolutional neural network , precision and recall , receiver operating characteristic , identification (biology) , data mining , machine learning , biology , immunology , image (mathematics) , botany
Automated classification of flow cytometry data has the potential to reduce errors and accelerate flow cytometry interpretation. We desired a machine learning approach that is accurate, is intuitively easy to understand, and highlights the cells that are most important in the algorithm's prediction for a given case.