Tuesday September 20, 2016

Deep Learning Drops Error Rate for Breast Cancer Diagnoses by 85%

If there’s one thing doctors and patients want from breast cancer diagnoses, it’s reliability. Keeping up with the massive flow of research data on breast cancer is a challenge for scientists. And the variety of methods used to analyze that data make reliable predictions difficult to come by. A team from Harvard Medical School’s Beth Israel Deaconess Medical Center (BIDMC) tackled this issue using deep learning, in the 2016 Camelyon Grand Challenge. Hosted by the International Symposium on Biomedical Imaging, the competition aims to determine how algorithms can help pathologists better identify cancer in lymph node images. The team’s results were dramatic, dropping the human error rate in diagnosis by 85% when aiding a pathologist’s efforts with GPU-powered deep learning analysis.