[Regular Paper] MVPNets: Multi-viewing Path Deep Learning Neural Networks for Magnification Invariant Diagnosis in Breast Cancer

Abstract

Breast cancer diagnosis requires a pathologist to analyze the histology slides under various magnifications. An automated diagnosis method to aid pathologists that is magnification independent will significantly save time, reduce cost and mitigate subjectivity and errors in current histopathological diagnosis procedures. This paper presents a deep learning network, called MVPNet and a customized data augmentation technique, called NuView, for magnification independent diagnosis. MVPNet is tailored to tackle the most common issues (diversity, relatively small size of datasets and manifestation of diagnostic biomarkers at various magnification levels) with breast cancer histology data to perform the classification. The network simultaneously analyzes local and global features of a given tissue image. It does so by viewing the tissue at varying levels of relative nuclei sizes. MVPNet has significantly less parameters than standard transfer learning deep models with comparable performance and it combines and processes local and global features simulatenously for effective diagnosis. Additionally, NuView extracts tumor nuclei location and points the attention of MVPNet to the informative region specifically. The method gives an average magnification independent classification accuracy of 92.2% as compared to 83% reported in literature on the BreaKHis database.