Maximum Mean Discrepancy Kerenels

最大平均差异核

• 批准号: 2882934
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2882934
• 关键词: Maximum; Mean; Discrepancy; Kerenels

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.

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（WSIs）进行各种诊断和预后任务，这些任务通常通过深度学习方法进行。然而，由于与多千兆像素WSI相关的存储器要求，病理学家必须首先将图像划分为较小的块，这意味着这些方法只能生成块级预测分数。这是以丢失分布在大于补丁大小的尺度上的信息为代价的。为了获得WSI级别的预测，我们使用了这些分数上的常见聚合技术，例如max-pooling。在这项研究中，我们建议，我们从不同的角度来处理聚合问题-使用最大平均离散度（MMD）作为衡量整个WSI之间的（不）相似性。我们假设，（不）两个WSI之间的相似性，可以通过简单地表示每个WSI作为其组成补丁的一组功能，其MMD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放入内存的单个矩阵中。这些内核允许我们使用广泛研究的内核方法来执行多种任务，例如预测建模，聚类和包外预测。据我们所知，这是第一次报告的WSI级内核在这一领域，因此，这项工作有可能铺平道路，一个新的分支的CPath。我们在最近的论文中提出了可行性证明，其中我们使用MMD内核实现了癌症基因组图谱计划（TCGA）中乳腺癌患者TP 53突变预测和生存分析的最新结果。然而，这项工作必须在更全面的研究中得到扩展。除此之外，还没有对幻灯片级别上的WSI之间的相似性度量进行广泛研究。因此，该指标的开发在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，该度量可用于数据库中的类似WSI查找，该数据库通常用于训练新病理学家识别癌症类型。此外，其他扩展包括与不同癌症类型合作，测量CPath中的域转移，回归任务和生存分析。