Artificial intelligence (AI) holds real potential for improving both the speed and accuracy of medical diagnostics. But before clinicians can harness the power of AI to identify conditions in images such as X-rays, they have to ‘teach’ the algorithms what to look for.
Identifying rare pathologies in medical images has presented a persistent challenge for researchers, because of the scarcity of images that can be used to train AI systems in a supervised learning setting.
Professor Shahrokh Valaee in The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE) and his team have designed a new approach: using machine learning to create computer generated X-rays to augment AI training sets.
Full article at: UofT Engineering News