Artificial intelligence achieves near-human performance in diagnosing breast cancer

#artificialintelligence

Pathologists have been largely diagnosing disease the same way for the past 100 years, by manually reviewing images under a microscope. But new work suggests that computers can help doctors improve accuracy and significantly change the way cancer and other diseases are diagnosed. A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) recently developed artificial intelligence (AI) methods aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explained pathologist Andrew Beck, MD, PhD, Director of Bioinformatics at the Cancer Research Institute at Beth Israel Deaconess Medical Center (BIDMC) and an Associate Professor at Harvard Medical School. "This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain's neocortex, the region where thinking occurs."


UMebnu

#artificialintelligence

A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) recently developed artificial intelligence (AI) methods aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explained pathologist Andrew Beck, MD, PhD, Director of Bioinformatics at the Cancer Research Institute at Beth Israel Deaconess Medical Center (BIDMC) and an Associate Professor at Harvard Medical School. In an objective evaluation in which researchers were given slides of lymph node cells and asked to determine whether or not they contained cancer, the team's automated diagnostic method proved accurate approximately 92 percent of the time, explained Khosla, adding, "This nearly matched the success rate of a human pathologist, whose results were 96 percent accurate." "But the truly exciting thing was when we combined the pathologist's analysis with our automated computational diagnostic method, the result improved to 99.5 percent accuracy," said Beck.


Artificial intelligence achieves near-human performance in diagnosing breast cancer

#artificialintelligence

A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) recently developed artificial intelligence (AI) methods aimed at training computers to interpret pathology images, with the long-term goal of building AI-powered systems to make pathologic diagnoses more accurate. "Our AI method is based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explained pathologist Andrew Beck, MD, PhD, Director of Bioinformatics at the Cancer Research Institute at Beth Israel Deaconess Medical Center (BIDMC) and an Associate Professor at Harvard Medical School. "This approach teaches machines to interpret the complex patterns and structure observed in real-life data by building multi-layer artificial neural networks, in a process which is thought to show similarities with the learning process that occurs in layers of neurons in the brain's neocortex, the region where thinking occurs." The Beck lab's approach was recently put to the test in a competition held at the annual meeting of the International Symposium of Biomedical Imaging (ISBI), which involved examining images of lymph nodes to decide whether or not they contained breast cancer. The research team of Beck and his lab's post-doctoral fellows Dayong Wang, PhD and Humayun Irshad, PhD, and student Rishab Gargya, together with Aditya Khosla of the MIT Computer Science and Artificial Intelligence Laboratory, placed first in two separate categories, competing against private companies and academic research institutions from around the world.


Artificial Intelligence Gets an A for Accuracy Diagnosing Breast Cancer - Breast Cancer News

#artificialintelligence

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.


AI computers could soon be used to diagnose cancer

#artificialintelligence

Computers could soon be helping to diagnose cancer in patients with the help of artificial intelligence that has been trained to spots the early signs of the disease. An AI machine capable of accurately diagnosing breast cancer 92 per cent of the time has been developed by researchers. While it is still not quite as good as human specialists – who are correct 96 per cent of the time – it suggests that AI could soon be used to speed up and improve cancer screening. Scientists have used machine learning to create an artificial intelligence system capable of diagnosing breast cancer from lymph node biopsies with 92 per cent accuracy (cancer cells in a lymph node pictured). When combined with a human pathologist this accuracy increased to 99.5 per cent The system was developed by computer scientists at Harvard Medical School gave a machine learning algorithm slides of lymph nodes from breast cancer patients.