本の詳細
形式
キンドル
ページ数
505
言語
英語
公開されました
Sep 29, 2021
出版社
Springer
ISBN-10
3030882101
ISBN-13
9783030882105
説明
This volume captures the proceedings of the inaugural MICCAI Workshop, focusing on the burgeoning field of deep generative models. It showcases a collection of research that delves into innovative methodologies for data augmentation, labeling complexities, and inherent imperfections within datasets. The contributors, comprised of leading experts in machine learning and medical imaging, present their findings on how these models can enhance the understanding of data and improve outcomes in the medical field.
With a blend of theoretical insights and practical applications, the book serves as a valuable resource for researchers and practitioners alike. It explores how advancements in generative models can address existing challenges in data processing while also paving the way for future explorations in this dynamic area of study. Through rigorous peer review, the content ensures high-quality scholarly discourse essential for anyone vested in the intersection of computer science and medical imaging.
With a blend of theoretical insights and practical applications, the book serves as a valuable resource for researchers and practitioners alike. It explores how advancements in generative models can address existing challenges in data processing while also paving the way for future explorations in this dynamic area of study. Through rigorous peer review, the content ensures high-quality scholarly discourse essential for anyone vested in the intersection of computer science and medical imaging.