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by two primary challenges: the lack of large-scale labeled datasets for white blood cell (WBC) segmentation and outdated segmentation methods. To address the first challenge, a semi-supervised learning framework should be brought to efficiently annotate the large dataset. In this work, we address this issue by proposing a novel self-training pipeline with the incorporation of FixMatch. 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.

翻译：医疗领域的人工智能，特别是在白细胞癌症诊断中，面临两大挑战：缺乏大规模标注的白细胞分割数据集，以及分割方法落后。为应对第一个挑战，应采用半监督学习框架对大规模数据集进行高效标注。本研究通过提出一种融合FixMatch的新型自训练流程来解决该问题。我们发现，在自训练流程中引入FixMatch能在多数情况下提升性能。采用基于DeepLab-V3架构和ResNet-50的一致性自训练方案，我们在Zheng 1、Zheng 2和LISC数据集上分别达到了90.69%、87.37%和76.49%的最佳性能。