Mammography Classification and Nodule Detection using Deep Neural Networks

Mammographic screenings are the most common modality for an early detection of breast cancer, but a robust annotation of the depicted breast tissue presents an ongoing challenge, even for well-experienced radiologists. Computer-aided diagnosis systems can support the human classification. Modern systems therefore often rely on deep-learning based methods. This thesis investigates the fully automatic on-image classification of mammograms into one of the classes benign, malignant (cancerous) or normal. In this context, we compare two different design paradigms, one straightforward end-to-end model with a more complex decomposition hierarchy. While the end-to-end model consists mainly of the deep-learning based classifier, the decomposition pipeline incorporates multiple stages i.e. a region of interest detection (realized as a fully convolutional architecture) followed by a classification stage. Contrary to initial expectations, the end-to-end classifier turned out to obtain a superior performance in terms of accuracy (end-to-end: 76.57 %, decomposition: 65.66 %, computed as mean over all three classes in a one vs. all evaluation) and an improved area under receiver operating characteristic-score. All discussed parametric models were trained from scratch without using pre-trained network weights. Therefore we discuss the choice of hyper-parameters, initialization, and choice of a feasible cost function. For a successful feature extraction, in the region of interest detection stage, the negative dice coefficient proved itself to be a more robust cost function than the also investigated sensitivity-specificity loss.