Depending on which radiologist analyzes a mammogram, there’s a huge variation in breast density readings — an assessment that indicates a patient’s risk for developing breast cancer. One study found that radiologists classified anywhere between six and 85 percent of mammograms into the higher cancer risk areas of “heterogeneously dense” or “extremely dense.”
Researchers at MIT are using neural networks to reduce this variation in radiologists’ interpretation of mammograms. Their deep learning model is being used by radiologists in Massachusetts General Hospital’s screening centers. It’s the first time such a model has been deployed in the daily workflow of a large-scale clinical practice, according to the researchers.
