2 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
A Continual Learning Approach for Cross-Domain White Blood Cell Classification
Accurate classification of white blood cells in peripheral blood is essential
for diagnosing hematological diseases. Due to constantly evolving clinical
settings, data sources, and disease classifications, it is necessary to update
machine learning classification models regularly for practical real-world use.
Such models significantly benefit from sequentially learning from incoming data
streams without forgetting previously acquired knowledge. However, models can
suffer from catastrophic forgetting, causing a drop in performance on previous
tasks when fine-tuned on new data. Here, we propose a rehearsal-based continual
learning approach for class incremental and domain incremental scenarios in
white blood cell classification. To choose representative samples from previous
tasks, we employ exemplar set selection based on the model's predictions. This
involves selecting the most confident samples and the most challenging samples
identified through uncertainty estimation of the model. We thoroughly evaluated
our proposed approach on three white blood cell classification datasets that
differ in color, resolution, and class composition, including scenarios where
new domains or new classes are introduced to the model with every task. We also
test a long class incremental experiment with both new domains and new classes.
Our results demonstrate that our approach outperforms established baselines in
continual learning, including existing iCaRL and EWC methods for classifying
white blood cells in cross-domain environments.Comment: Accepted for publication at workshop on Domain Adaptation and
Representation Transfer (DART) in International Conference on Medical Image
Computing and Computer Assisted Intervention (MICCAI 2023