Meta Learning With Medical Imaging and Health Informatics Applications

by Hien Van Nguyen (editor), Ronald Summers (Editor), Rama Chellappa (Editor)

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. First book on applying Meta Learning to medical imaging Pioneers in the field as contributing authors to explain the theory and its development Has GitHub repository consisting of various code examples and documentation to help the audience to set up Meta-Learning algorithms for their applications quickly

399 pages, Kindle Edition

Published September 24, 2022

Reviews

[G Osborne · Rating 2 out of 5]

Poor managed book. the sample algorithm in the book start to misalign with the sample codes given in the original paper, I had hard time to follow ... it seems the same equations are not properly tested as well, this can be seen from open issues on the equations and author is not responding to the open issue reported. Some of the issues have been left unattended for months.