Probabilistic visual search for masses within mammography images using deep learning

We developed a deep learning-based visual search system for the task of automated search and localization of masses in whole mammography images. The system consists of two modules: a classification engine and a localization engine. It first classifies mammograms as containing a mass or no mass using a deep learning classifier, and then localizes the mass(es) within the image using a regional probabilistic approach based on a deep learning network. We obtained 85% accuracy for the task of identifying images that contain a mass, and we were able to localize 85% of the masses at an average of 0.9 false positives per image. Our system has the advantages of being able to work with an entire mammography image as input without the need for image segmentation or other pre-processing steps, such as cropping or tiling the image, and it is based on deep learning with unsupervised feature discovery, so it does not require pre-defined and hand-crafted image features.