Call for Chapters: Meta-Learning Frameworks for Imaging Applications


Ashok Sharma, University of Jammu, India
Sandeep Sengar, Cardiff Metropolitan University, United Kingdom
Parveen Singh, Cluster University Jammu, India

Call for Chapters

Proposals Submission Deadline: May 22, 2023
Full Chapters Due: July 3, 2023


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. In recent years, meta-learning, or learning to learn, has become increasingly fashionable. Meta-Learning creates computational mechanisms to methodically and efficiently adapt to new tasks, rather than creating AI systems from the ground up for each machine learning activity. Deep neural networks face several fundamental limitations, including a high data demand, computationally expensive training, and limited ability for task transfer.

Meta-learning, often known as learning to learn, is the discipline of methodically examining how different machine learning algorithms perform on a variety of learning tasks and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. This not only speeds up and improves the design of machine learning pipelines and neural networks, but also allows us to replace hand-engineered algorithms with fresh data-driven approaches.

The meta-learning paradigm has the potential to address these issues. This book will present overview of the Meta-Learning framework with imaging applications. Many Book Chapters will include meta-learning as a unifying theory, as well as common versions including model-agnostic learning, memory augmentation, prototype networks, and learning to optimize.

The book will bring together many experts from the domains of machine learning and imaging application to explore the current state of Meta-Learning, its application to medical imaging and health informatics, and its future directions. This book will give an overview of the Meta-Learning framework in Imaging Application


· This book gives a quick overview of Meta-Learning theories and their various applications in medical imaging and health informatics.
· The book will also concentrates on several types of machine learning model in alignment to meta-learning method in general, which may be used in a variety of machine learning techniques.
· A full description of the experimental techniques and evaluation meta learning frameworks will be included because meta-learning necessitates executing many different processes with the goal of achieving performance outcomes

Target Audience

Machine learning researchers, biomedical engineers, medical practitioners, and graduate students who wish to learn the basics of Meta-Learning methods and how to apply them to medical imaging and health informatics applications

Recommended Topics

• Multidimensional artificial intelligence
• Machine vision and information exchange
• Novel machine learning framework.
• Application of the Meta Learning framework to address the regression-analysis-based needs in MRI.
• Advantages of the Meta Learning framework in realistic scenarios.
• Reducing number of data sets needed for reaching similar training and performance accuracy and precision.
• Addressing multiple tasks without compromising the performance accuracy and precision of Meta Learning Models
• Optimization approaches in Meta Learning Models
• Vision driven soft computing Meta Learning methods

Submission Procedure

Researchers and practitioners are invited to submit on or before May 22, 2023, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by June 12, 2023 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted by July 3, 2023, and all interested authors must consult the guidelines for manuscript submissions at prior to submission. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, Meta-Learning Frameworks for Imaging Applications. All manuscripts are accepted based on a double-blind peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.


This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), an international academic publisher of the "Information Science Reference" (formerly Idea Group Reference), "Medical Information Science Reference," "Business Science Reference," and "Engineering Science Reference" imprints. IGI Global specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit This publication is anticipated to be released in 2023.

Important Dates

May 22, 2023: Proposal Submission Deadline
June 12, 2023: Notification of Acceptance
July 3, 2023: Full Chapter Submission
July 24, 2023: Review Results Returned
August 7, 2023: Final Acceptance Notification
August 21, 2023: Final Chapter Submission


Ashok Sharma
University of Jammu

Sandeep Sengar
Cardiff Metropolitan University

Parveen Singh
Cluster University Jammu


Computer Science and Information Technology; Education; Medical, Healthcare, and Life Sciences; Science and Engineering
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