De Novo Identification and Visualization of Important Cell Populations for Classic Hodgkin Lymphoma Using Flow Cytometry and Machine Learning | Zendy

Paul D. Simonson | Zendy; Yue Wu | Zendy; David Wu | Zendy; Jonathan R. Fromm | Zendy; Aaron Lee | Zendy

Open Access

De 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 , identification (biology) , lymphoma , visualization , computational biology , computer science , biology , pathology , artificial intelligence , medicine , immunology , 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.

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