Breast cancer is the second leading cancer-related cause of death among women in the U.S. It’s estimated that in 2015, 232,000 women were diagnosed with the disease and approximately 40,000 died from it. And while diagnostic exams like mammography have come into wide practice — in 2014, over 39 million breast cancer screenings were performed in the U.S. alone — they’re not always reliable. About 10 to 15 percent of women who undergo a mammogram are asked to return following an inconclusive analysis.

That’s why researchers at New York University are investigating an AI-driven technique that promises much higher precision than today’s tests. In a newly published paper on Arxiv.org (““), they describe a deep convolutional neural network — a class of machine learning algorithm

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