Abstract
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.
Abstract (translated)
核分裂分析提供了对病理切片图像分析有价值的信息。然而,不同核型的外观巨大差异导致识别核型的困难。大多数基于神经网络的方法受到卷积局部响应场的直接影响,并较少关注核的空间分布或核的不规则形态。在本文中,我们首先提出了一种独特的多边形结构特征学习机制,将核的轮廓转换为按顺序采样的点序列,并使用循环神经网络将关键点之间的Sequential change聚合起来以获得可学习的形状特征。接下来,我们将病理切片图像转换为以核作为节点的 graph 结构,并构建一个 graph 神经网络,将核的空间分布嵌入其表示中。为了捕捉核分类类别及其周围组织模式之间的相关关系,我们进一步引入了边缘特征,它们被定义为相邻核的背景纹理。最后,我们将多边形和 graph 结构学习机制集成到一个整体框架中,以提取核内和核间结构特征,用于核分类。实验结果显示,与当前最好的方法相比,我们提出的框架取得了显著的改进。
URL
https://arxiv.org/abs/2302.11416
