A newly developed artificial intelligence system is showing promise as a way to help pathologists improve diagnosis of breast cancer from images. In a test at a scientific meeting, it boosted human accuracy from 96 to 99.5 percent.
The artificial intelligence (AI) system is "based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explains Andrew Beck, an associate professor in pathology at Harvard Medical School, who heads the team developing the new system at Beth Israel Deaconess Medical Center (BIDMC), in Boston, MA.
Prof. Beck and colleagues demonstrated the new AI system in a competition held at the annual meeting of the International Symposium of Biomedical Imaging (ISBI 2016) in Prague in April.
He and his colleagues are developing AI methods that train computers to interpret pathology images to improve the accuracy of diagnoses.
The approach they are using teaches computers to interpret the complex patterns seen in such images by "building multi-layer artificial neural networks," says Prof. Beck.
The process is thought to be similar to the way learning takes place in the layers of neurons in the neocortex of the brain, the region where thinking occurs.
The team put the new AI system to the test at the ISBI 2016 meeting by getting it to examine images of lymph nodes to decide whether or not they showed evidence of breast cancer.
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