Generalizable self-supervised learning for imaging flow cytometry on multi-dataset leukocyte differential

Imaging flow cytometry with supervised learning can realize high-accuracy leukocyte classification. However, since supervised learning relies on annotated cell images and this labeling process generates cell losses, current imaging flow cytometry with supervised learning cannot be used for leukocyte differential. This study proposes a self-supervised contrastive learning framework pretrained on a non-annotated dataset comprising bright-field leukocyte images acquired via a custom imaging flow cytometry. The pretrained feature extractor, frozen during downstream tasks, employs lightweight linear or multilayer perceptron classification heads to achieve leukocyte differential in three independent annotated datasets: normal leukocyte subtypes (4-class), leukemia cells (2-class), and mixed normal/abnormal leukocytes (8-class). The proposed method achieves classification accuracies of 96.16%, 96.68%, and 92.24% across three independent datasets, performing comparably to supervised baselines (with differences of less than 2%), therefore demonstrating high accuracy for leukocyte differentials. Furthermore, since the abnormal leukocytes in the latter two datasets were not included in the pre-training dataset, the corresponding F1-scores of 96.68% and 91.92% indicate the method’s strong generalization ability. The presented self-supervised learning framework enables high-accuracy and strong-generalization leukocyte differential using non-annotated images from imaging flow cytometry.