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Matek, C. ; Schwarz, S.* ; Spiekermann, K.* ; Marr, C.

Human-level recognition of blast cells in acute myeloid leukemia with convolutional neural networks.

Nat. Mach. Intell. 1, 538-544 (2019)
Postprint DOI
Open Access Green
Reliable recognition of malignant white blood cells is a key step in the diagnosis of haematologic malignancies such as acute myeloid leukaemia. Microscopic morphological examination of blood cells is usually performed by trained human examiners, making the process tedious, time-consuming and hard to standardize. Here, we compile an annotated image dataset of over 18,000 white blood cells, use it to train a convolutional neural network for leukocyte classification and evaluate the network’s performance by comparing to inter- and intra-expert variability. The network classifies the most important cell types with high accuracy. It also allows us to decide two clinically relevant questions with human-level performance: (1) if a given cell has blast character and (2) if it belongs to the cell types normally present in non-pathological blood smears. Our approach holds the potential to be used as a classification aid for examining much larger numbers of cells in a smear than can usually be done by a human expert. This will allow clinicians to recognize malignant cell populations with lower prevalence at an earlier stage of the disease.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
ISSN (print) / ISBN 2522-5839
e-ISSN 2522-5839
Zeitschrift Nature machine intelligence
Quellenangaben Band: 1, Heft: , Seiten: 538-544 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort [London]
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)