Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the state-of-the-art methods.
翻译:内核分类为生理病理学图像分析提供了宝贵的信息。 但是, 不同核类型外观的巨大变化在外观上造成了辨别核的难题。 多数神经网络方法都受到局部可接受变化领域的影响, 较少注意核的空间分布或核核的不规律轮廓形状。 在本文中, 我们首先提出一个新的多边形结构特征学习机制, 将核等离子转化为按顺序取样的点序列, 并使用一个经常性的神经网络, 将关键点之间的相继变化集中起来, 以获得可学习的形状特征。 最后, 我们把一个直方和图形结构学习机制转换成一个以核为节点的图形结构结构, 并建立一个图形神经网络, 将核的空间分布与其周围组织模式联系起来。 为了捕捉核心的类别与其周围组织模式之间的相互关系, 我们进一步引入了边缘特征, 这些特征被定义为相邻核核之间的背景纹理。 最后, 我们将多边和图形结构学习机制整合成一个整体框架, 以核心为核心为核心之间的显著的实验性模型, 并显示内部和核心之间的结构分类。