Artificial Intelligence Gets an A+ for Accuracy Diagnosing Breast Cancer

Artificial Intelligence Gets an A+ for Accuracy Diagnosing Breast Cancer
A team of researchers at the Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) in Boston have been working on developing artificial intelligence (AI) tools with potential to significantly change and improve accuracy in cancer and other disease diagnosis. Noting that pathology methods for diagnosing disease have stayed largely the same for the past 100 years with tissue samples manually reviewed under a microscope, the investigative work suggests diagnostic accuracy can be improved by using computers to interpret pathology images. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," said Dr. Andrew Beck director of Bioinformatics at the Cancer Research Institute at Beth Israel Deaconess Medical Center (BIDMC) in press release. Beck, who is also an associate professor at Harvard Medical School said the approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks thought to be similar to how the learning occurs in the brain neocortex, where thinking occurs. The Beck lab's approach was recently tested in a competition at the annual meeting of the International Symposium of Biomedical Imaging (ISBI) held in Prague, Czech Republic, in April. The test task involved examining lymph node images to determine whether or not breast cancer was present. Beck, post-doctoral fellows Dayong Wang and Humayun Irshad, and student Rishab Gargya, with Aditya Khosla, of MIT Computer Science and Artificial Intelligence Laboratory, placed first in two of the test categories. "Identifying the presence or absence of metastatic cancer in a patients ly
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