  Artificial Intelligence (AI) in healthcare, especially in white blood cell
cancer diagnosis, is hindered by two primary challenges: the lack of
large-scale labeled datasets for white blood cell (WBC) segmentation and
outdated segmentation methods. These challenges inhibit the development of more
accurate and modern techniques to diagnose cancer relating to white blood
cells. To address the first challenge, a semi-supervised learning framework
should be devised to efficiently capitalize on the scarcity of the dataset
available. In this work, we address this issue by proposing a novel
self-training pipeline with the incorporation of FixMatch. Self-training is a
technique that utilizes the model trained on labeled data to generate
pseudo-labels for the unlabeled data and then re-train on both of them.
FixMatch is a consistency-regularization algorithm to enforce the model's
robustness against variations in the input image. We discover that by
incorporating FixMatch in the self-training pipeline, the performance improves
in the majority of cases. Our performance achieved the best performance with
the self-training scheme with consistency on DeepLab-V3 architecture and
ResNet-50, reaching 90.69%, 87.37%, and 76.49% on Zheng 1, Zheng 2, and LISC
datasets, respectively.
