Exploring Few-Shot Object Detection on Blood Smear Images: A Case Study of Leukocytes and Schistocytes
Journal:
arXiv
Published Date:
Mar 21, 2025
Abstract
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.
