Finding Corresponding Regions In Different Mammography Projections Using Convolutional Neural Networks ; Prediktion av Motsvarande Regioner i Olika Mammografiprojektioner med Faltningsnätverk

Mammography screenings are performed regularly on women in order to detect early signs of breast cancer, which is the most common form of cancer. During an exam, X-ray images (called mammograms) are taken from two different angles and reviewed by a radiologist. If they find a suspicious lesion in one of the views, they confirm it by finding the corresponding region in the other view. Finding the corresponding region is a non-trivial task, due to the different image projections of the breast and different angles of compression needed during the exam. This thesis explores the possibility of using deep learning, a data-driven approach, to solve the corresponding regions problem. Specifically, a convolutional neural network (CNN) called U-net is developed and trained on scanned mammograms, and evaluated on both scanned and digital mammograms. A model based method called the arc model is developed for comparison. Results show that the best U-net produced better results than the arc model on all evaluated metrics, and succeeded in finding the corresponding area 83.9% of times, compared to 72.6%. Generalization to digital images was excellent, achieving an even higher score of 87.6%, compared to 83.5% for the arc model.