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.
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 Radiology and Academic Radiology. He was a co-chair of the Computer-aided Diagnosis program of the annual SPIE Medical Imaging conference in 2010 and 2011. He has co-authored over 300 journal, review and conference proceedings articles, and is a co-inventor on 12 patents. Prof. Rama Chellappa is a Bloomberg Distinguished Professor in the Departments of Electrical and Computer Engineering (Whiting School of Engineering) and Biomedical Engineering (School of Medicine) with a secondary appointment in the Department of Computer Science at Johns Hopkins University (JHU). At JHU, he is also affiliated with CIS, CLSP, IAA, Malone Center, and MINDS. Before coming to JHU in August 2020, he was a Distinguished University Professor, a Minta Martin Professor of Engineering, and a Professor in the ECE department and a Permanent Member at the University of Maryland Institute Advanced Computer Studies at the University of Maryland (UMD). He holds a non-tenure position as a College Park Professor in the ECE department at UMD. His current researcher interests are computer vision, pattern recognition, machine intelligence and artificial intelligence. He received the K. S. Fu Prize from the International Association of Pattern Recognition (IAPR). He is a recipient of the Society, Technical Achievement, and Meritorious Service Awards from the IEEE Signal Processing Society and four IBM Faculty Development Awards. He also received the Technical Achievement and Meritorious Service Awards from the IEEE Computer Society. He received the Inaugural Leadership Award from the IEEE Biometrics Council and the 2020 IEEE Jack S. Kilby Medal for Signal Processing. At UMD, he received college and university level recognitions for research, teaching, innovation, and mentoring of undergraduate students. He has been recognized as an Outstanding ECE by Purdue University and as a Distinguished Alumni by the Indian Institute of Science, India. He served as the Editor-in-Chief of PAMI. He is a Golden Core Member of the IEEE Computer Society, served as a Distinguished Lecturer of the IEEE Signal Processing Society and as the President of the IEEE Biometrics Council. He is a Fellow of AAAI, AAAS, ACM, AIMBE, IAPR, IEEE, NAI, and OSA and holds eight patents.
Title: Meta Learning With Medical Imaging and Health Informatics Applications (The MICCAI Society book Series)
Author:
ISBN: 9780323998512
Binding:
Publisher: Elsevier Science & Technology
Publication Date: 2022-09-29
Number of Pages: 428
Weight: 0.4492 kg
