用少量血涂片图像实现白细胞与血吸虫卵的精准检测
Exploring Few-Shot Object Detection on Blood Smear Images: A Case Study of Leukocytes and Schistocytes
- 基于DE-ViT的少样本学习框架，仅用少量图像训练
- 在血吸虫卵数据集上，Faster R-CNN X101表现最佳
- 研究揭示了跨领域数据差异对模型性能的影响
血液疾病诊断常依赖于特定血细胞数量的量化，其变化可能提示病理状态。因此，开发精确的自动血细胞计数系统至关重要。本文提出一种名为DE-ViT的新方法，应用于少样本检测场景，仅使用少量图像进行训练。实验在两个数据集上进行：用于白细胞检测的Raabin-WBC数据集和本地构建的Schistocyte Identification Dataset（SC-IDB）。将DE-ViT与两种基线模型（Faster R-CNN 50、Faster R-CNN X 101）对比。尽管DE-ViT在COCO和LVIS数据集上表现领先，但在Raabin-WBC上其性能低于两个基线模型。仅Faster R-CNN X 101在SC-IDB上获得满意结果。性能差异可能源于领域偏移问题。
原文摘要 · Abstract (English)
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.
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