Title: AI and Deep Learning in Medical Imaging and Genomics: Lessons from ENIGMA’s Global Studies of Brain Diseases
Abstract: AI and deep learning are rapidly advancing, with new mathematics and algorithms being developed daily to discover patterns in medical imaging data. AI can aid diagnosis, prognosis, and disease subtyping, and can show us how to tackle previously unimaginable problems, such as ‘cracking the brain’s genetic code’. With examples from two large scale initiatives – ENIGMA and AI4AD – we describe some of the triumphs and challenges in learning from medical imaging data collected across the world. Since 2009, ENIGMA (1) performed the largest neuroimaging studies of 13 major brain diseases, from Parkinson’s, epilepsy and ataxia to schizophrenia, depression, PTSD, autism, ADHD and OCD, and (2) led the largest genetic studies of the human brain. Over 2000 scientists take part in ENIGMA’s 51 working groups, pooling data from over 45 countries, and suggesting and tackling new problems. Deep learning methods including CNNs and RNN variants, variational autoencoders, and generative adversarial networks (GANs) are being applied to this data to answer key questions about the brain. We cover the basic ideas, pitfalls, and challenges in applying these machine learning and deep learning methods to diverse biomedical data worldwide. We illustrate some lessons learned from multisite machine learning projects that deal with data imbalance, domain shift and adaptation, and federated learning, as well as some unsolved problems and opportunities to take part in international machine learning challenges.