Super-resolution of mammograms

Abstract

High-quality mammography is the most effective technology presently available for breast cancer screening. High resolution mammograms usually lead to more accurate diagnoses; however, they require large doses of radiation, which may have harmful effects. In this paper, we present a method to synthesize high-resolution mammograms from low-resolution inputs, which offers the potential of allowing accurate diagnoses while minimizing risks to patients. Our algorithm combines statistical machine learning methods and stochastic search to learn the mapping from low-resolution to high-resolution mammograms using a large dataset of training image pairs. Experimental results show that the super-resolution algorithm can generate high-quality, high-resolution breast mammograms from low-resolution input with no human intervention.