Medical Applications with Disentanglements: First MICCAI Workshop, MAD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings

The book summarizes the presentations and discussions from the First MICCAI Workshop on Medical Applications with Disentanglements,MAD 2022,which took place in Singapore in September 2022. It includes full papers and short papers on topics such as GANs, VAEs, and normalizing-flow architectures,with applications in brain age prediction, skull reconstruction, and unsupervised pathology disentanglement.

Format: Paperback / softback
Length: 127 pages
Publication date: 01 February 2023
Publisher: Springer International Publishing AG

The First MICCAI Workshop on Medical Applications with Disentanglements, MAD 2022, held in conjunction with MICCAI 2022 in Singapore on September 22, 2022, has resulted in a comprehensive collection of post-conference proceedings. This book encompasses a total of 8 full papers, along with one short paper, which have undergone meticulous review. These papers delve into various topics, including generative adversarial networks (GAN), variational autoencoders (VAE), and normalizing-flow architectures, as well as a diverse array of medical applications. These applications span brain age prediction, skull reconstruction, and unsupervised pathology disentanglement, showcasing the workshop's commitment to advancing healthcare technologies.

The contributions presented in this book highlight the latest advancements and innovative approaches in medical image analysis and interpretation. The authors have extensively researched and developed state-of-the-art techniques, leveraging deep learning models and computational algorithms to address challenging healthcare problems. The topics covered in this book are of utmost importance in the field of medical imaging, as they have the potential to revolutionize the diagnosis, treatment, and monitoring of various medical conditions.

One of the key highlights of this book is the exploration of generative adversarial networks (GAN). GANs have gained significant attention in recent years for their ability to generate realistic and high-quality images, which can be useful in various medical applications, such as image reconstruction, drug discovery, and medical imaging analysis. The papers in this book discuss the latest developments in GAN architecture, training strategies, and applications in medical imaging.

Variational autoencoders (VAE) are another important topic covered in this book. VAEs are used for dimensionality reduction and feature learning in medical image analysis. The papers in this book explore the use of VAEs for tasks such as brain age prediction, skull reconstruction, and unsupervised pathology disentanglement. The authors demonstrate how VAEs can effectively extract meaningful features from medical images and improve the accuracy of various medical applications.

Normalizing-flow architectures are also discussed in this book. Normalizing-flow architectures are used for modeling complex and non-linear relationships between different variables in medical images. The papers in this book explore the use of normalizing-flow architectures for tasks such as image denoising, shape estimation, and motion tracking. The authors demonstrate how normalizing-flow architectures can improve the performance of medical image analysis tasks and enable more accurate predictions and diagnoses.

In addition to these technical topics, the book also includes a wide range of medical applications. The papers cover various medical domains, such as brain aging, skull reconstruction, cancer diagnosis, and cardiovascular disease detection. The authors present novel approaches and algorithms for these applications, demonstrating their potential to improve healthcare outcomes and patient care.

Overall, the First MICCAI Workshop on Medical Applications with Disentanglements, MAD 2022, has produced a valuable collection of papers that showcase the latest advancements in medical image analysis and interpretation. The book provides a comprehensive overview of generative adversarial networks, variational autoencoders, and normalizing-flow architectures, as well as a diverse range of medical applications. The contributions made by the authors in this book are expected to have a significant impact on the field of medical imaging and healthcare in the years to come.

Weight: 226g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031250453
Edition number: 1st ed. 2023