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Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning

Name: Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning
Brand: Springer Nature Switzerland AG
SKU: 9783030605476
Price: 50.13 EUR
Availability: InStock

Autor: Daguang Xu, Wenqi Li, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Ziyue Xu, Fausto Milletari, Nicola Rieke, Holger Roth

Jazyk:

Väzba: Brožovaná

Vydavateľ: Springer Nature Switzerland AG

Dostupnosť: Skladom u dodávateľa

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This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation an...

Informácie o knihe

Autor

Daguang Xu, Wenqi Li, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Ziyue Xu, Fausto Milletari, Nicola Rieke, Holger Roth

Jazyk

Angličtina

Väzba

Kniha - Brožovaná

Vydalo

2020

Stránok

212

EAN

9783030605476

ISBN

3030605477

Enbook ID

33249480

Vydavateľ

Springer Nature Switzerland AG

Hmotnosť

355

Rozmery

155 x 235 x 13

Kompletný popis

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. For DART 2020, 12 full papers were accepted from 18 submissions. They deal with methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings by making them robust and consistent across different domains. For DCL 2020, the 8 papers included in this book were accepted from a total of 12 submissions. They focus on the comparison, evaluation and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases; where information privacy is a priority; where it is necessary to deliver strong guarantees on the amount and nature of private information that may be revealed by the model as a result of training; and where it's necessary to orchestrate, manage and direct clusters of nodes participating in the same learning task.

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Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning

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