The detection of blood disorders often hinges upon the quantification of specific blood cell types. Variations in cell counts may indicate the presence of pathological conditions. Thus, the significance of developing precise automatic systems for blood cell enumeration is underscored. The investigation focuses on a novel approach termed DE-ViT. This methodology is employed in a Few-Shot paradigm, wherein training relies on a limited number of images. Two distinct datasets are utilised for experimental purposes: the Raabin-WBC dataset for Leukocyte detection and a local dataset for Schistocyte identification. In addition to the DE-ViT model, two baseline models, Faster R-CNN 50 and Faster R-CNN X 101, are employed, with their outcomes being compared against those of the proposed model. While DE-ViT has demonstrated state-of-the-art performance on the COCO and LVIS datasets, both baseline models surpassed its performance on the Raabin-WBC dataset. Moreover, only Faster R-CNN X 101 yielded satisfactory results on the SC-IDB. The observed disparities in performance may possibly be attributed to domain shift phenomena.

翻译：血液疾病的检测常依赖于特定血细胞类型的定量分析。细胞计数的变化可能提示病理状况的存在。因此，开发精确的血细胞自动计数系统具有重要意义。本研究聚焦于一种名为DE-ViT的新方法。该方法应用于少样本范式，其训练仅依赖于少量图像。实验使用了两个不同的数据集：用于白细胞检测的Raabin-WBC数据集和用于裂红细胞识别的本地数据集。除DE-ViT模型外，还采用了两个基线模型Faster R-CNN 50和Faster R-CNN X 101，并将其结果与所提模型进行比较。尽管DE-ViT在COCO和LVIS数据集上展现了最先进的性能，但两个基线模型在Raabin-WBC数据集上的表现均优于该模型。此外，仅Faster R-CNN X 101在SC-IDB数据集上取得了令人满意的结果。观察到的性能差异可能归因于领域偏移现象。