  Accurate morphological classification of white blood cells (WBCs) is an
important step in the diagnosis of leukemia, a disease in which nonfunctional
blast cells accumulate in the bone marrow. Recently, deep convolutional neural
networks (CNNs) have been successfully used to classify leukocytes by training
them on single-cell images from a specific domain. Most CNN models assume that
the distributions of the training and test data are similar, i.e., the data are
independently and identically distributed. Therefore, they are not robust to
different staining procedures, magnifications, resolutions, scanners, or
imaging protocols, as well as variations in clinical centers or patient
cohorts. In addition, domain-specific data imbalances affect the generalization
performance of classifiers. Here, we train a robust CNN for WBC classification
by addressing cross-domain data imbalance and domain shifts. To this end, we
use two loss functions and demonstrate their effectiveness in
out-of-distribution (OOD) generalization. Our approach achieves the best F1
macro score compared to other existing methods and is able to consider rare
cell types. This is the first demonstration of imbalanced domain generalization
in hematological cytomorphology and paves the way for robust single cell
classification methods for the application in laboratories and clinics.
