论文标题
结肠核实例分割使用概率的两阶段检测器
Colon Nuclei Instance Segmentation using a Probabilistic Two-Stage Detector
论文作者
论文摘要
癌症是发达国家死亡的主要原因之一。癌症诊断是通过对可疑组织样本的微观分析进行的。这个过程耗时且容易发生，但是深度学习模型在癌症诊断过程中可能会有所帮助。我们建议将Centernet2对象检测模型更改为执行实例分割，我们称为Segcenternet2。我们在Conic Challenge数据集中训练Segcenternet2，并表明它在竞争指标中的性能比Mask R-CNN更好。
Cancer is one of the leading causes of death in the developed world. Cancer diagnosis is performed through the microscopic analysis of a sample of suspicious tissue. This process is time consuming and error prone, but Deep Learning models could be helpful for pathologists during cancer diagnosis. We propose to change the CenterNet2 object detection model to also perform instance segmentation, which we call SegCenterNet2. We train SegCenterNet2 in the CoNIC challenge dataset and show that it performs better than Mask R-CNN in the competition metrics.
