Persistent Homology for Breast Tumor Classification using Mammogram Scans
This work addresses breast cancer diagnosis for medical imaging, but it is incremental as it applies existing topological methods to a specific domain.
The paper tackled breast tumor classification from mammogram scans by using persistent homology with multiple persistence diagram representations based on local binary patterns, achieving over 90% sensitivity for abnormal detection in two datasets.
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.