论文标题
用点注释的细胞检测的边界盒先验
Bounding Box Priors for Cell Detection with Point Annotations
论文作者
论文摘要
单个细胞类型的大小（例如红细胞）在人类中的变化不大。我们将此知识用作仅具有几个地面真理框盒注释的图像中分类和检测细胞的先验，而大多数单元则用点注释。这种设置导致弱半监督的学习。我们建议在训练过程中使用随机（ST）框或边界框预测替换点。所提出的“ Mean-iou” ST Box最大程度地将所有框架属于样品空间的框具有最大的重叠，并具有特定于类的边界框的特定于类近似近似的先验概率分布。与现有的方法不同，我们的方法与框和点标记的图像一起训练，与现有方法不同，这些方法先用盒子和点标记的图像训练。在最具挑战性的环境中，当只有5％的图像被盒装标记时，尿液数据集上的定量实验表明，我们的一个阶段方法比5.56 MAP优于两阶段方法。此外，我们建议一种方法部分回答“需要多少个盒子标记的注释？”在训练机器学习模型之前。
The size of an individual cell type, such as a red blood cell, does not vary much among humans. We use this knowledge as a prior for classifying and detecting cells in images with only a few ground truth bounding box annotations, while most of the cells are annotated with points. This setting leads to weakly semi-supervised learning. We propose replacing points with either stochastic (ST) boxes or bounding box predictions during the training process. The proposed "mean-IOU" ST box maximizes the overlap with all the boxes belonging to the sample space with a class-specific approximated prior probability distribution of bounding boxes. Our method trains with both box- and point-labelled images in conjunction, unlike the existing methods, which train first with box- and then point-labelled images. In the most challenging setting, when only 5% images are box-labelled, quantitative experiments on a urine dataset show that our one-stage method outperforms two-stage methods by 5.56 mAP. Furthermore, we suggest an approach that partially answers "how many box-labelled annotations are necessary?" before training a machine learning model.
