论文标题
组织病理学图像中的深度学习框架
A Deep Learning Framework for Nuclear Segmentation and Classification in Histopathological Images
论文作者
论文摘要
细胞核分割和分类是数字病理处理工作流程中的先决条件。但是，由于其高级异质性和广泛的变化，这非常具有挑战性。这项工作提出了一个深层神经网络，以同时实现核分类和分割，该网络是使用带有三个不同分支的统一框架设计的，包括分割，悬停映射和分类。分割分支旨在产生每个核的边界。悬停分支计算核像素与质量中心的水平和垂直距离。核分类分支用于区分从分割获得的细胞核内部的像素类别。
Nucleus segmentation and classification are the prerequisites in the workflow of digital pathology processing. However, it is very challenging due to its high-level heterogeneity and wide variations. This work proposes a deep neural network to simultaneously achieve nuclear classification and segmentation, which is designed using a unified framework with three different branches, including segmentation, HoVer mapping, and classification. The segmentation branch aims to generate the boundaries of each nucleus. The HoVer branch calculates the horizontal and vertical distances of nuclear pixels to their centres of mass. The nuclear classification branch is used to distinguish the class of pixels inside the nucleus obtained from segmentation.
