.2M crowdsourced contest aims to improve breast-cancer detection through deep machine …
Current breast-cancer screening methods result in too many false positives, causing unnecessary anxiety and additional testing, and creating additional cost to the healthcare system. (Digital Mammography DREAM Challenge) Breast-cancer screening using mammograms certainly saves lives, but too many women receive false positives from the test and — even worse — some cancers are missed. Now Seattle researchers and healthcare providers are leading a global, X-prize style contest that could ultimately result in big improvements in breast-cancer diagnoses. They’re helping organize the crowdsourced event that asks commercial and nonprofit organizations to develop algorithms that use deep machine learning to more accurately identify breast cancers. Diana Buist, the director of Research and Strategic Partnership for Seattle’s Group Health Research Institute In addition to providing an opportunity to help combat a leading cause…
Link to Full Article: .2M crowdsourced contest aims to improve breast-cancer detection through deep machine …
$1.2M crowdsourced contest aims to improve breast-cancer detection through deep machine …
