A team of researchers from Beth Israel Deaconess Medical Centre and Harvard Medical School won a competition after developing a way of training deep learning to read pathology images.
The International Symposium on Biomedical Imaging set the challenge between October 2015 to April 2016 to encourage research into identifying breast cancer by computers rather than by pathologists.
Since the nineteenth century, the primary tool used to identify cells has always been the microscope but the report, by the Harvard team, identified many problems with this system.
These included a lack of standardization across the board, diagnosis errors and the time it takes for pathologists to manually load millions of slides each year.
Utilisting deep learning, and feeding the machine hundreds of slides showing both cancerous and non-cancerous lymph nodes, scientists were able to train AI to pick out hazardous cells.
Using this technique they were able to make the AI accurate in 92 per cent of diagnosis and decrease the human rate of error by 85 per cent.
Importantly, the errors made by the deep learning system did not generally correlate with the errors made by humans.
The report concluded: “Although the pathologist alone is currently superior to our deep learning system alone, combining deep learning with the pathologist produced a major reduction in pathologist error rate.”
“These results suggest that integrating deep learning-based approaches into the work-flow of the diagnostic pathologist could drive improvements in the reproducibility, accuracy and clinical value of pathological diagnoses.”
Artificial Intelligence is commonly used to regonise speech, images and objects.
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