Data Engineering in Medical Imaging (Paperback)

Binod Bhattarai

ISBN 10: 3032080088 / ISBN 13: 9783032080080

Published by Springer Nature Switzerland AG, Cham, 2025

About this Item

Paperback. This book constitutes the proceedings of the Third MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2025, held in conjunction with the 28th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, in Daejeon, South Korea, on September 27, 2025.The 24 full papers included in this book were carefully reviewed and selected from 33 submissions. These papers focus on the topic of data engineering in medical imaging and address open questions in the field. The workshop welcomes various approaches such as data and label augmentation, active learning and active synthesis, federated learning, multimodal learning, self-supervised learning, and large-scale data management and data quality assessment. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9783032080080

Bibliographic Details

• Title: Data Engineering in Medical Imaging (Paperback)
• Publisher: Springer Nature Switzerland AG, Cham
• Publication Date: 2025
• Language: English
• ISBN 10: 3032080088
• ISBN 13: 9783032080080
• Binding: Paperback
• Condition: new

About this title

Synopsis

This book constitutes the proceedings of the Third MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2025, held in conjunction with the 28th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, in Daejeon, South Korea, on September 27, 2025.

The 24 full papers included in this book were carefully reviewed and selected from 33 submissions. These papers focus on the topic of data engineering in medical imaging and address open questions in the field. The workshop welcomes various approaches such as data and label augmentation, active learning and active synthesis, federated learning, multimodal learning, self-supervised learning, and large-scale data management and data quality assessment.