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Published by Springer Nature Switzerland AG, CH, 2021
ISBN 10: 3030882098 ISBN 13: 9783030882099
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Published by Springer Nature Switzerland AG, Cham, 2025
ISBN 10: 3032054710 ISBN 13: 9783032054715
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Paperback. Condition: new. Paperback. This book constitutes the proceedings of the 5th Workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention, DGM4MICCAI 2025, held in conjunction with the 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, in Daejeon, South Korea, during September 2025.The 33 papers presented in this book were carefully reviewed and selected from 50 submissions. These papers deal with recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by Springer, Berlin|Springer Nature Switzerland|Springer, 2023
ISBN 10: 3031185757 ISBN 13: 9783031185755
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ISBN 10: 3031727436 ISBN 13: 9783031727436
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ISBN 10: 3030882098 ISBN 13: 9783030882099
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Condition: New. This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in Oct.
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ISBN 10: 3031185757 ISBN 13: 9783031185755
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore.DG4MICCAI 2022 accepted 12 papers from the 15 submissions received. The workshop focusses on recent algorithmic developments, new results, andpromising future directions in Deep Generative Models. Deep generative modelssuch as Generative Adversarial Network (GAN) and Variational Auto-Encoder(VAE) are currently receiving widespread attention from not only the computervision and machine learning communities, but also in the MIC and CAI community.
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