Meta Learning With Medical Imaging and Health Informatics Applications

Meta-Learning is a paradigm that constructs computational mechanisms to adapt AI systems to new tasks efficiently. It has the potential to address deep neural networks challenges, such as data requirements, training, and transfer. This book summarizes Meta-Learning theories and their applications in medical imaging and health informatics, covering unifying theory, model-agnostic learning, memory augmentation, prototypical networks, and learning to optimize.

Format: Paperback / softback
Length: 428 pages
Publication date: 30 September 2022
Publisher: Elsevier Science & Technology

In recent years, meta-learning, also known as learning to learn, has gained significant popularity. Instead of creating AI systems from scratch for each machine learning task, meta-learning develops computational mechanisms to efficiently adapt to new tasks. This paradigm holds immense potential in addressing the fundamental challenges faced by deep neural networks, including the demanding requirement for extensive data, the computationally expensive training process, and the limited ability to transfer knowledge across tasks.

This book offers a concise overview 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 esteemed thought leaders from the fields of machine learning and health informatics to explore the current state of meta-learning, its relevance to medical imaging and health informatics, and the emerging directions for future research.

By leveraging meta-learning, we can develop more robust and adaptable AI systems that can learn from diverse datasets and adapt to new environments. This has significant implications for healthcare, where accurate and efficient diagnosis and treatment require the ability to learn from large amounts of medical data. Meta-learning also has the potential to improve the efficiency and effectiveness of medical imaging techniques, such as MRI and CT scans, by enabling the adaptation of algorithms to specific patient populations and disease conditions.

However, there are also challenges associated with meta-learning, such as the need for more robust evaluation metrics and the development of effective training strategies. Additionally, there are ethical considerations to address, particularly in the field of medical imaging, where the use of personal data and sensitive information requires careful consideration.

Overall, meta-learning holds great promise for advancing the field of AI and healthcare. By continuing to explore and develop this paradigm, we can unlock new opportunities for personalized medicine, improved patient outcomes, and a deeper understanding of the human body and disease.

Weight: 906g
Dimension: 192 x 236 x 27 (mm)
ISBN-13: 9780323998512