Training artificial intelligence with artificial X-rays: New research could help AI identify rare conditions in medical images by augmenting existing datasets

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 and his team have designed a new approach: using machine learning to create computer generated X-rays to augment AI training sets. "In a sense, we are using machine learning to do machine learning," says Valaee, a professor in The Edward S. Rogers Sr. "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. "AI has the potential to help in a myriad of ways in the field of medicine," says Valaee.