Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry.

Lewis, Joshua E; Cooper, Lee A D; Jaye, David L; Pozdnyakova, Olga

Lewis, Joshua E; Cooper, Lee A D; Jaye, David L; Pozdnyakova, Olga.

Affiliation

Lewis JE; Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts.
Cooper LAD; Department of Pathology, Northwestern University, Chicago, Illinois.
Jaye DL; Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia.
Pozdnyakova O; Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts. Electronic address: opozdnyakova@bwh.harvard.edu.

Mod Pathol ; 37(1): 100373, 2024 Jan.

Article in En | MEDLINE | ID: mdl-37925056

Subject(s)

Deep Learning; Leukemia, Myeloid, Acute; Humans; Flow Cytometry/methods; Leukemia, Myeloid, Acute/diagnosis; Leukemia, Myeloid, Acute/genetics; Leukemia, Myeloid, Acute/metabolism; Acute Disease; Cytogenetics

Key words

acute myeloid leukemia; attention-based multi-instance learning models; automated deep-learning based analysis; automated diagnosis; flow cytometry

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Leukemia, Myeloid, Acute / Deep Learning Limits: Humans Language: En Journal: Mod Pathol Journal subject: PATOLOGIA Year: 2024 Type: Article

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