Meta learning with medical imaging and health informatics applications edited by Hien Van Nguyen, Ronald Summers, Rama Chellappa

Format:

Book

Contributor:

Hien Van Nguyen, editor.

Summers, Ronald Marc, editor.

Chellappa, Rama, editor.

Series:

Elsevier and MICCAI Society book series

The Elsevier and Miccai Society book series

Language:

English

Subjects (All):

Medical informatics.

Machine learning.

Diagnostic imaging.

Machine Learning.

Diagnostic Imaging.

Medical Subjects:

Machine Learning.

Diagnostic Imaging.

Physical Description:

1 online resource

Place of Publication:

London Academic Press [2023]

Summary:

"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"-- From ScienceDirect

Contents:

Learning to learn in medical applications: A journey through optimization / Azade Farshad, Yousef Yeganeh and Nassir Navab

Introduction to meta learning / Pengyu Yuan and Hien Van Nguyen

Metric learning algorithms for meta learning / Pengyu Yuan and Hien Van Nguyen

Meta learning by optimization / Pengyu Yuan and Hien Van Nguyen

Model-based meta learning / Pengyu Yuan and Hien Van Nguyen

Few-shot chest x-ray diagnosis using discriminative ensemble learning / Angshuman Paul, Yu-Xing Tang, ... Ronald M. Summers

Domain generalization of deep networks for medical image segmentation via meta learning / Quande Liu, Qi Dou, ... Pheng-Ann Heng

Meta learning for adaptable lung nodule image analysis / Aryan Mobiny and Hien Van Nguyen

Few-shot segmentation of 3D medical images / Abhijit Guha Roy, Shayan Siddiqui, ... Christian Wachinger

Smart task design for meta learning medical image analysis systems: Unsupervised, weakly-supervised, and cross-domain design of meta learning tasks / Cuong C. Nguyen, Youssef Dawoud, ... Gustavo Carneiro

AGILE - a meta learning framework for few-shot brain cell classification / Pengyu Yuan and Hien Van Nguyen

Few-shot learning for dermatological disease diagnosis / Viraj Prabhu, Anitha Kannan, ... Xavier Amatriain

Knowledge-guided meta learning for disease prediction / Qiuling Suo, Hyun Jae Cho, ... Aidong Zhang

Case study: few-shot pill recognition: How to train an AI model to recognize a new category of pill from only a few samples like humans? / Andreas Pastor, Suiyi Ling, ... Patrick Le Callet

Meta learning for anomaly detection in fundus photographs / Sarah Matta, Mathieu Lamard, ... Gwenolé Quellec

Rare disease classification via difficulty-aware meta learning / Xiaomeng Li, Lequan Yu, ... Pheng-Ann Heng

Improved MR image reconstruction using federated learning / Pengfei Guo, Puyang Wang, ... Vishal M. Patel

Neural architecture search for medical image applications / Viet-Khoa Vo-Ho, Kashu Yamazaki, ... Ngan Le

Meta learning in the big data regime: Applications to transfer learning and few shot learning / Swami Sankaranarayanan and Yogesh Balaji

Notes:

Includes bibliographical references and index

Online resource; title from PDF title page (ScienceDirect, viewed August 11, 2025)

Other Format:

Print version Meta learning with medical imaging and health informatics applications

ISBN:

9780323998529

0323998526

OCLC:

1346349011

Access Restriction:

Restricted for use by site license