Maximum Mean Discrepancy Kerenels

计算病理学（CPath）中一个相关但困难的领域是使用常规组织学全切片图像（wsi）进行各种诊断和预后任务，通常通过深度学习方法进行。然而，由于与数十亿像素wsi相关的内存需求，病理学家必须首先将图像划分为更小的补丁，这意味着这些方法只能生成补丁级别的预测分数。这是以丢失分布在比补丁大小更大的尺度上的信息为代价的。为了获得wsi级别的预测，我们对这些分数使用常见的聚合技术，例如max-pooling。在这项研究中，我们建议从不同的角度来处理聚合问题-使用最大平均差异（MMD）作为整个wsi之间（非）相似性的度量。我们假设两个WSI之间的（非）相似性可以通过简单地将每个WSI表示为其组成补丁的一组特征来捕获。这样的度量允许生成wsi级别的内核，这些内核将整个数据集的关系压缩到可以装入内存的单个矩阵中。这些核允许我们使用广泛研究的核方法来执行大量的任务，如预测建模、聚类和袋外预测。据我们所知，这是该领域WSI级别内核的第一份报告，因此，这项工作有可能为CPath的新分支铺平道路。我们在最近的一篇论文中提出了可行性证明，我们使用MMD内核实现了癌症基因组图谱计划（TCGA）中TP53突变预测和乳腺癌患者生存分析的最先进结果。然而，这项工作必须在更全面的研究中得到扩展。除此之外，在幻灯片层面上wsi之间的相似性指标还没有得到广泛的研究。因此，该指标的发展在CPath的各种现有但具有挑战性的领域中具有很大的潜力。例如，该指标可用于数据库中类似的WSI查找，该数据库通常用于培训新的病理学家识别癌症类型。此外，其他扩展还包括处理不同的癌症类型、测量CPath中的域位移、回归任务和生存分析。

A relevant yet difficult area in Computational Pathology (CPath) is using routine histology Whole Slide Images (WSIs) for various diagnostic and prognostic tasks often approached via deep learning methods. However, due to the memory requirements associated with multi-gigapixel WSIs pathologists must first divide images into smaller patches meaning these methods can only generate patch level prediction scores. This comes at the price of losing information distributed over scales greater than the patch size. To get WSI-level predictions we make use of common aggregation techniques on these scores such as max-pooling. In this research, we propose that we approach the aggregation problem from a different perspective - the use of Maximum Mean Discrepancy (MMD) as a measure of (dis)similarity between entire WSIs. We hypothesize that (dis)similarity between two WSIs can be captured by their MMD by simply representing each WSI as a set of features of its constituent patches. Such a metric allows for the generation of WSI-level kernels that condense the relationships of an entire dataset into a single matrix which can fit into memory. These kernels allow us to use widely researched kernel methods in order to perform a multitude of tasks such as predictive modelling, clustering and out of bag prediction. To the best of our knowledge, this is the first report of WSI level kernels in this domain and, as such, this work has the potential to pave the way for a new branch of CPath. We have presented a proof of feasibility in our recent paper where we used MMD kernels to achieve state-of-the-art results for TP53 mutation prediction and survival analysis of breast cancer patients from The Cancer Genome Atlas Program (TCGA). However, this work has to be extended in a more comprehensive study. On top of this similarity metrics between WSIs on the slide level has not been studied extensively. Thus, the development of this metric has a lot of potential in various existing yet challenging domains of CPath. For example, the metric can be used for similar WSI lookup in a database which is often utilised to train new pathologists to recognise cancer types. Furthermore, other extensions include working with different cancer types, measuring domain shift in CPath, regression tasks and survival analysis.