Exploring Few-Shot Object Detection on Blood Smear Images: A Case Study of Leukocytes and Schistocytes
This work addresses automated blood cell detection for medical diagnostics, but it is incremental as it adapts existing methods to new data with mixed results.
The study tackled few-shot object detection for leukocytes and schistocytes in blood smear images using a novel DE-ViT method, but baseline models like Faster R-CNN X 101 outperformed it on specific datasets, with performance gaps attributed to domain shift.
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.