Abstract
How similar are two images? In computational pathology, where Whole Slide Images (WSIs) of digitally scanned tissue samples from patients can be multi-gigapixels in size, determination of degree of similarity between two WSIs is a challenging task with a number of practical applications. In this work, we explore a novel strategy based on kernelized Maximum Mean Discrepancy (MMD) analysis for determination of pairwise similarity between WSIs. The proposed approach works by calculating MMD between two WSIs using kernels over deep features of image patches. This allows representation of an entire dataset of WSIs as a kernel matrix for WSI level clustering, weakly-supervised prediction of TP-53 mutation status in breast cancer patients from their routine WSIs as well as survival analysis with state of the art prediction performance. We believe that this work will open up further avenues for application of WSI-level kernels for predictive and prognostic tasks in computational pathology.
Abstract (translated)
两个图像有多相似?在计算病理学中,数字扫描患者组织样本的整张切片图像(WSIs)可以变得很大,因此确定两个WSIs之间的相似性是一项具有实际应用挑战的任务。在本研究中,我们探索了一种基于内核化最大平均差异分析(MMD)的新策略,以确定两个WSIs之间的相对相似性。我们提出的策略通过使用内核对图像斑点的深特征计算MMD来计算。这允许将整个WSIs数据集表示为一个内核矩阵,用于WSI级别的簇集表示, weakly-supervised从常规WSIs预测乳腺癌患者TP-53突变状态,以及使用最先进的预测性能的生存分析。我们相信,这项工作将打开更多途径,用于应用WSI级别的内核在计算病理学中预测和预后任务。
URL
https://arxiv.org/abs/2301.09624
