Recently, the use of circle representation has emerged as a method to improve
the identification of spherical objects (such as glomeruli, cells, and nuclei)
in medical imaging studies. In traditional bounding box-based object detection,
combining results from multiple models improves accuracy, especially when
real-time processing isn't crucial. Unfortunately, this widely adopted strategy
is not readily available for combining circle representations. In this paper,
we propose Weighted Circle Fusion (WCF), a simple approach for merging
predictions from various circle detection models. Our method leverages
confidence scores associated with each proposed bounding circle to generate
averaged circles. Our method undergoes thorough evaluation on a proprietary
dataset for glomerular detection in object detection within whole slide imaging
(WSI). The findings reveal a performance gain of 5 %, respectively, compared to
existing ensemble methods. Furthermore, the Weighted Circle Fusion technique
not only improves the precision of object detection in medical images but also
notably decreases false detections, presenting a promising direction for future
research and application in pathological image analysis.

我们提出了一种称为加权圆融合（Weighted Circle Fusion，WCF）的简单方法，用于合并来自不同圆检测模型的预测。该方法利用每个建议的边界圆关联的置信分数生成平均圆，在医学图像中改进了目标检测的精度，并显著减少了误检，为病理图像分析领域的未来研究和应用提供了有希望的方向。