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Sinopsis
Meta-learning, or learning to learn, has been gaining popularity in recent years to adapt to new tasks systematically and efficiently in machine learning. In the book, Meta-Learning Frameworks for Imaging Applications , experts from the fields of machine learning and imaging come together to explore the current state of meta-learning and its application to medical imaging and health informatics. The book presents an overview of the meta-learning framework, including common versions such as model-agnostic learning, memory augmentation, prototype networks, and learning to optimize. It also discusses how meta-learning can be applied to address fundamental limitations of deep neural networks, such as high data demand, computationally expensive training, and limited ability for task transfer. One critical topic in imaging is image segmentation, and the book explores how a meta-learning-based framework can help identify the best image segmentation algorithm, which would be particularly beneficial in the healthcare domain. This book is relevant to healthcare institutes, e-commerce companies, and educational institutions, as well as professionals and practitioners in the intelligent system, computational data science, network applications, and biomedical applications fields. It is also useful for domain developers and project managers from diagnostic and pharmacy companies involved in the development of medical expert systems. Additionally, graduate and master students in intelligent systems, big data management, computational intelligent approaches, computer vision, and biomedical science can use this book for their final projects and specific courses.
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Sandeep Singh Sengar is a Lecturer in Computer Science at Cardiff Metropolitan University, United Kingdom. He also holds the position of Cluster Leader for Computer Vision/Image Processing at this place. Before joining this position, he worked as a Postdoctoral Research Fellow at the Machine Learning Section of the Computer Science Department, at the University of Copenhagen, Denmark (a ranked #1 university in Denmark). He completed his Ph.D. degree in Computer Vision at the Department of Computer Science and Engineering from the Indian Institute of Technology (ISM), Dhanbad, India, and an M. Tech. degree from Motilal Nehru National Institute of Technology, Allahabad, India. He is also a Fellow of HEA, a Senior Member of IEEE, and a Professional Member of ACM. Dr. Sengar’s broader research interests include Machine/Deep Learning, Computer Vision, Image/Video Processing, and its applications. He has published several research articles in reputable international journals and conferences. He is an Editorial Board Member, Guest Editor, and Reviewer at reputed International Journals. He is a reviewer of research grants at EPSRC and Cardiff Met.
Parveen Singh is an Associate Professor and Head Department of Computer Sciences,Govt SPMR College of Commerce, Jammu, honoured with National Award by Govt of India, for popularization of Science among children’s, is a dynamic instructor and thought leader, focused on providing students with a rigorous and challenging education. Earned recognition as a knowledgeable teacher, author along with as a debater with well-organized, stimulating, and student-centred courses. Cultivated teaching partnerships and alliances with key business contacts across the J&k . Co-authored 09 Books and attended more than 17 international and national conferences. Certified, Research Based Pedagogical tools Trainer from Centre Of Excellence in Science and Mathematics Education IISER pune. Awarded with Govt. of J&K, Science Innovative Teacher Award 2013, Awarded with Govt of Tamil Nadu ICTACT Best Techno Faculty Award 2016, Awarded with Principal Citation for contribution towards Growth of Institution 2010,Awarded with Distinguished HOD award 2017, by Computer Society of India, CSI, Mumbai Chapter, Member of Jammu University Academic council from 2006-2009,Member of Board of Studies, Jammu University since 2003.
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- EditorialIGI Global
- Año de publicación2023
- ISBN 10 1668476592
- ISBN 13 9781668476598
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de páginas276
- EditorSharma Ashok, Sengar Sandeep Singh, Singh Parveen
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Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Meta-learning, or learning to learn, has been gaining popularity in recent years to adapt to new tasks systematically and efficiently in machine learning. In the book, Meta-Learning Frameworks for Imaging Applications , experts from the fields of machine learning and imaging come together to explore the current state of meta-learning and its application to medical imaging and health informatics. The book presents an overview of the meta-learning framework, including common versions such as model-agnostic learning, memory augmentation, prototype networks, and learning to optimize. It also discusses how meta-learning can be applied to address fundamental limitations of deep neural networks, such as high data demand, computationally expensive training, and limited ability for task transfer. One critical topic in imaging is image segmentation, and the book explores how a meta-learning-based framework can help identify the best image segmentation algorithm, which would be particularly beneficial in the healthcare domain. This book is relevant to healthcare institutes, e-commerce companies, and educational institutions, as well as professionals and practitioners in the intelligent system, computational data science, network applications, and biomedical applications fields. It is also useful for domain developers and project managers from diagnostic and pharmacy companies involved in the development of medical expert systems. Additionally, graduate and master students in intelligent systems, big data management, computational intelligent approaches, computer vision, and biomedical science can use this book for their final projects and specific courses. Nº de ref. del artículo: 9781668476598
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