Classification of Breast Density in Mammograms Using Deep Machine Learning

Type: MA thesis

Status: finished

Date: May 14, 2018 - November 14, 2018

Supervisors: Sulaiman Vesal, Nishant Ravikumar, Andreas Maier, Dr. Ludiwg Ritschl (siemens-healthineers), Tino Haderlein

The female breast is mainly composed of adipose and fibroglandular tissue. In a mammogram, fibroglandular tissue appears brighter than fatty tissue and is therefore called “dense”. Current clinical protocol requires radiologists to not only detect possible cancer tumors but also to evaluate breast density in a mammogram \cite{Wockel.2018}, which corresponds to the relative amount of fibroglandular tissue. Breast density is an important characteristic of a mammogram because it is a breast cancer risk marker and it affects the mammogram’s sensitivity. The evaluation is done via classification into one of the four categories defined by the “Breast Imaging – Reporting and Data System” guidelines from the American College of Radiology (ACR BI-RADS).

In this thesis, the application of convolutional neural networks for the classification of breast density in mammograms is investigated. Several neural network architectures and training methods are tested and the results compared against classical machine learning methods. A strategy for the removal of possibly noisy labels in the training data is presented and an analysis of inter-observer variability among radiologists is carried out. It is found that the algorithm with the best classification performance provides breast density assessment on level with an average experienced radiologist.