标题： 一种具有空间依赖性的半参数高斯混合模型及其在全幻灯片图像聚类分析中的应用 显示英文标题

标题： A Semiparametric Gaussian Mixture Model with Spatial Dependence and Its Application to Whole-Slide Image Clustering Analysis

摘要： 我们在这里开发了一个半参数高斯混合模型（SGMM），用于考虑有价值的空间信息的无监督学习。 具体而言，我们假设每个实例有一个随机位置。 然后，在这个随机位置的条件下，我们假设特征向量遵循标准高斯混合模型（GMM）。 所提出的SGMM允许混合概率与空间位置非参数地相关。 与经典GMM相比，SGMM更加灵活，并允许同一类别的实例在空间上聚集。 为了估计SGMM，开发了新颖的EM算法，并建立了严格的渐近理论。 进行了大量数值模拟以展示我们的有限样本性能。 对于一个实际应用，我们将SGMM方法应用于CAMELYON16数据集的全幻灯片图像（WSIs）以进行乳腺癌检测。 SGMM方法表现出卓越的聚类性能。 显示英文摘要

摘要： We develop here a semiparametric Gaussian mixture model (SGMM) for unsupervised learning with valuable spatial information taken into consideration. Specifically, we assume for each instance a random location. Then, conditional on this random location, we assume for the feature vector a standard Gaussian mixture model (GMM). The proposed SGMM allows the mixing probability to be nonparametrically related to the spatial location. Compared with a classical GMM, SGMM is considerably more flexible and allows the instances from the same class to be spatially clustered. To estimate the SGMM, novel EM algorithms are developed and rigorous asymptotic theories are established. Extensive numerical simulations are conducted to demonstrate our finite sample performance. For a real application, we apply our SGMM method to the CAMELYON16 dataset of whole-slide images (WSIs) for breast cancer detection. The SGMM method demonstrates outstanding clustering performance.