论文标题
使用乳房X线照片扫描进行乳腺肿瘤分类的持续同源
Persistent Homology for Breast Tumor Classification using Mammogram Scans
论文作者
论文摘要
现场拓扑数据分析中的一个重要工具称为持续同源（pH），用于以持久图（PD）的形式编码不同分辨率的数据同源性的抽象表示。在这项工作中，我们基于具有里程碑意义的选择方法（称为局部二进制模式）来构建一个以上的单个图像表示，该方法从图像中编码了不同类型的本地纹理。我们使用持久性景观，持久图像，持久式（Betti曲线）和统计数据采用了不同的PD矢量化。我们使用乳房X线照片扫描测试了提议的基于地标的pH值对两个公开可用的乳房异常检测数据集的有效性。在两个数据集中，获得的基于地标的pH值的敏感性超过90％，以检测异常的乳房扫描。最后，实验结果为使用不同类型的PD矢量化提供了新的见解，这些PD矢量有助于将pH与机器学习分类器结合使用。
An Important tool in the field topological data analysis is known as persistent Homology (PH) which is used to encode abstract representation of the homology of data at different resolutions in the form of persistence diagram (PD). In this work we build more than one PD representation of a single image based on a landmark selection method, known as local binary patterns, that encode different types of local textures from images. We employed different PD vectorizations using persistence landscapes, persistence images, persistence binning (Betti Curve) and statistics. We tested the effectiveness of proposed landmark based PH on two publicly available breast abnormality detection datasets using mammogram scans. Sensitivity of landmark based PH obtained is over 90% in both datasets for the detection of abnormal breast scans. Finally, experimental results give new insights on using different types of PD vectorizations which help in utilising PH in conjunction with machine learning classifiers.
