本の詳細
形式
キンドル
ページ数
315
言語
英語
公開されました
Apr 26, 2024
出版社
Springer
説明
This book captures the vibrant discussions and cutting-edge research presented at the 3rd International Workshop on Data Augmentation, Labelling, and Imperfections held in conjunction with MICCAI 2023 in Vancouver. Featuring insights from a diverse group of contributors, it highlights the latest advancements and challenges in data handling, especially in the medical imaging field.
The authors explore innovative techniques and methodologies that address data imperfections and the complexities of reliable labelling practices. By combining theoretical frameworks with practical applications, they aim to enhance the quality of data used in machine learning and artificial intelligence, pushing the envelope of what's possible.
Readers will find a wealth of knowledge, from foundational concepts to the exploration of real-world implications. This volume serves not only as a valuable resource for researchers and practitioners but also as an invitation to reflect on the future of data science in the realm of medical imaging.
The authors explore innovative techniques and methodologies that address data imperfections and the complexities of reliable labelling practices. By combining theoretical frameworks with practical applications, they aim to enhance the quality of data used in machine learning and artificial intelligence, pushing the envelope of what's possible.
Readers will find a wealth of knowledge, from foundational concepts to the exploration of real-world implications. This volume serves not only as a valuable resource for researchers and practitioners but also as an invitation to reflect on the future of data science in the realm of medical imaging.