选择不同形态以分而治之吉像素病理图像
Selection of Distinct Morphologies to Divide & Conquer Gigapixel Pathology Images
AI总结：
本文提出SDM方法，通过选择不同形态的补丁子集（蒙太奇）来分而治之处理吉像素病理图像，实现紧凑且全面的WSI表示，在多个数据集上优于Yottixel，并无需经验参数化。
摘要：
全切片图像（WSI）是展示复杂组织结构的大规模数字病理文件。从每个WSI中选择一个小的、具有代表性的补丁子集至关重要且具有挑战性。因此，采用“分而治之”的方法对于促进计算病理学中的WSI分析（包括分类和WSI匹配）至关重要。为此，我们提出了一种名为“选择不同形态”（SDM）的新方法，用于选择WSI补丁的子集。其目标是涵盖给定WSI中的所有固有形态变化，同时最小化所选补丁的数量以代表这些变化，确保补丁集紧凑而全面。这个系统化策划的补丁集构成了我们称之为“蒙太奇”的东西。我们在各种公开和私有的组织病理学数据集上评估SDM蒙太奇的代表性。这是通过使用留一法WSI搜索和匹配评估方法进行的，并与最先进的Yottixel的镶嵌图进行比较。SDM在其评估中在所有数据集上表现出显著的有效性。此外，SDM通过内在优化选择过程以捕获WSI中的不同形态特征，消除了经验参数化的必要性，而这是Yottixel镶嵌图的一个关键方面。
英文摘要：
Whole slide images (WSIs) are massive digital pathology files illustrating intricate tissue structures. Selecting a small, representative subset of patches from each WSI is essential yet challenging. Therefore, following the "Divide & Conquer" approach becomes essential to facilitate WSI analysis including the classification and the WSI matching in computational pathology. To this end, we propose a novel method termed "Selection of Distinct Morphologies" (SDM) to choose a subset of WSI patches. The aim is to encompass all inherent morphological variations within a given WSI while simultaneously minimizing the number of selected patches to represent these variations, ensuring a compact yet comprehensive set of patches. This systematically curated patch set forms what we term a "montage". We assess the representativeness of the SDM montage across various public and private histopathology datasets. This is conducted by using the leave-one-out WSI search and matching evaluation method, comparing it with the state-of-the-art Yottixel's mosaic. SDM demonstrates remarkable efficacy across all datasets during its evaluation. Furthermore, SDM eliminates the necessity for empirical parameterization, a crucial aspect of Yottixel's mosaic, by inherently optimizing the selection process to capture the distinct morphological features within the WSI.
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