An algorithm based on a deep probabilistic architecture referred to as a tree-structured sum-product network (t-SPN) is considered for cell classification. The t-SPN is constructed such that the unnormalized probability is represented as conditional probabilities of a subset of most similar cell classes. The constructed t-SPN architecture is learned by maximizing the margin, which is the difference in the conditional probability between the true and the most competitive false label. To enhance the generalization ability of the architecture, L2-regularization (REG) is considered along with the maximum margin (MM) criterion in the learning process. To highlight cell features, this paper investigates the effectiveness of two generic high-pass filters: ideal high-pass filtering and the Laplacian of Gaussian (LOG) filtering. On both HEp-2 and Feulgen benchmark datasets, the t-SPN architecture learned based on the max-margin criterion with regularization produced the highest accuracy rate compared to other state-of-the-art algorithms that include convolutional neural network (CNN) based algorithms. The ideal high-pass filter was more effective on the HEp-2 dataset, which is based on immunofluorescence staining, while the LOG was more effective on the Feulgen dataset, which is based on Feulgen staining.

翻译：本文考虑一种基于深度概率架构的算法，该架构称为树状和积网络（t-SPN），用于细胞分类。t-SPN的设计使得非归一化概率表示为最相似细胞类别子集的条件概率。所构建的t-SPN架构通过最大化真实标签与最具竞争性错误标签之间的条件概率差值（即间隔）进行学习。为增强架构的泛化能力，学习过程中将L2正则化与最大间隔准则相结合。为突出细胞特征，本文研究了两种通用高通滤波器的有效性：理想高通滤波和高斯-拉普拉斯滤波。在HEp-2和Feulgen两个基准数据集上，基于带正则化的最大间隔准则学习的t-SPN架构相比其他包括卷积神经网络算法在内的最先进算法取得了最高准确率。基于免疫荧光染色的HEp-2数据集上理想高通滤波器效果更优，而基于Feulgen染色的Feulgen数据集上高斯-拉普拉斯滤波器更有效。