利用持续同调卷积进行组织病理切片分类
Classification of Histopathology Slides with Persistent Homology Convolutions
AI总结：
本文提出利用持续同调卷积对组织病理切片进行分类，通过提取局部拓扑信息提升分类性能。
摘要：
卷积神经网络（CNNs）是计算机视觉任务如图像分类中的标准工具。然而，典型模型架构可能导致拓扑信息的丢失。在特定领域如组织病理学中，拓扑学是一个重要的描述符，可以通过分析细胞的形状特征来区分具有疾病指示的组织。现有文献表明，通过持续同调重新引入拓扑信息可以提高医学诊断；然而，以前的方法使用全局拓扑总结，不包含拓扑特征局部性的信息。为解决这一差距，我们提出了一种新的方法，使用修改过的卷积运算符称为持续同调卷积生成局部持续同调数据。该方法捕捉了拓扑特征的局部性和平移等变性信息。我们使用各种组织病理切片的表示形式进行了比较研究，发现使用持续同调卷积训练的模型优于传统训练的模型，并且对超参数更不敏感。这些结果表明，持续同调卷积从组织病理切片中提取了有意义的几何信息。
英文摘要：
Convolutional neural networks (CNNs) are a standard tool for computer vision tasks such as image classification. However, typical model architectures may result in the loss of topological information. In specific domains such as histopathology, topology is an important descriptor that can be used to distinguish between disease-indicating tissue by analyzing the shape characteristics of cells. Current literature suggests that reintroducing topological information using persistent homology can improve medical diagnostics; however, previous methods utilize global topological summaries which do not contain information about the locality of topological features. To address this gap, we present a novel method that generates local persistent homology-based data using a modified version of the convolution operator called \textit{Persistent Homology Convolutions}. This method captures information about the locality and translation equivariance of topological features. We perform a comparative study using various representations of histopathology slides and find that models trained with persistent homology convolutions outperform conventionally trained models and are less sensitive to hyperparameters. These results indicate that persistent homology convolutions extract meaningful geometric information from the histopathology slides.
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