Academic Press Meta Learning With Medical Imaging and Health Informatics Applications (The MICCAI Society book Series)

Product Description Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, Meta-Learning constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks’ fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks. This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions. The book comes with a GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly. Review A great resource to learn Meta-learning methods and their application to Medical Imaging and Health Informatics From the Back Cover Meta-Learning, or learning to learn, has become increasingly popular in recent years. Instead of building AI systems from scratch for each machine learning task, <i>Meta-Learning</i> constructs computational mechanisms to systematically and efficiently adapt to new tasks. The meta-learning paradigm has great potential to address deep neural networks’ fundamental challenges such as intensive data requirement, computationally expensive training, and limited capacity for transfer among tasks.<br><br>This book provides a concise summary of Meta-Learning theories and their diverse applications in medical imaging and health informatics. It covers the unifying theory of meta-learning and its popular variants such as model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize. The book brings together thought leaders from both machine learning and health informatics fields to discuss the current state of Meta-Learning, its relevance to medical imaging and health informatics, and future directions. The book comes with a GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly. About the Author Dr. Hien Van Nguyen is an Assistant Professor of the Department of Electrical and Computer Engineering Department at the University of Houston. His research interests are at the intersection between machine learning, computer vision, and biomedical image analysis. He has published 45 peer-reviewed papers and received 12 U.S. patents. His research has received awards from the National Science Foundation and the National Institutes of Health. He has served as area chairs of MICCAI (2019, 2021) and organized a series of popular MICCAI tutorials including deep learning for medical imaging (2015), deep reinforcement learning for medical imaging (2018), Bayesian deep learning (2019). Dr. Summers received a BA in physics and the M.D. and Ph.D. degrees in medicine/anatomy and cell biology from the University of Pennsylvania. He completed a medical internship at the Presbyterian-University of Pennsylvania Hospital, Philadelphia, PA, a radiology residency at the University of Michigan, Ann Arbor, MI, and an MRI fellowship at Duke University. In 2000, he received the Presidential Early Career Award for Scientists and Engineers, presented by Dr. Neal Lane, President Clinton's science advisor. In 2012, he received the NIH Director's Award, presented by NIH Director Dr. Francis S. Collins. He is an editorial board member of the journals Radiolo