Training artificial intelligence with artificial X-rays: New research could help AI identify rare conditions in medical images by augmenting existing datasets
University of Toronto Faculty of Applied Science & Engineering
Professor Shahrokh Valaee and his team have designed a new approach: using machine learning to create computer generated X-rays to augment AI training sets. We are creating simulated X-rays that reflect certain rare conditions so that we can combine them with real X-rays to have a sufficiently large database to train the neural networks to identify these conditions in other X-rays. Valaee is a member of the Machine Intelligence in Medicine Lab (MIMLab), a group of physicians, scientists and engineering researchers who are combining their expertise in image processing, artificial intelligence and medicine to solve medical challenges. We’ve been able to show that artificial data generated by a deep convolutional GANs can be used to augment real datasets, says Valaee. It’s exciting because we’ve been able to overcome a hurdle in applying artificial intelligence to medicine by showing that these augmented datasets help to improve classification accuracy, says Valaee.
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