1 research outputs found
Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology
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., that the
data are independently and identically distributed. Therefore, they are not
robust to different staining protocols, 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 the effectiveness on 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.Comment: Published as a ICLR 2023 workshop paper: What do we need for
successful domain generalization