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：基于被称为树结构和产品网络(t-SPN)的深度概率结构的算法,被考虑用于细胞分类。T-SPN的构造使未正常化的概率代表了最相似的单元格类子类的有条件概率。建造的t-SPN的架构通过最大差值来学习,即真实标签和最具竞争力的假标签之间的有条件概率差异。为了提高结构的总体化能力,在学习过程中将L2-正规化(REG)与最大差值(MM)标准一起考虑。为了突出细胞特征,本文调查两种通用的高通路过滤器的功效:理想的高通路过滤器和高山(Laplaceian)过滤器。在Hep-2和Feulgen基准数据集中,根据最大差值标准(与正规化)学习的t-SPNPN结构产生了最高准确率,而其他州级算法则包括同级神经网络(CNN),为了突出的基点特征,本文调查了两种通用高通度高通度过滤器的功效:理想的高通路过滤器过滤器和更甚的ENS-BLOLA,其基点数据以更基于高通则以高端的SENS-ROLV数据为基础。</s>