Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Zakazov, Ivan"'
Generalizability of deep learning models may be severely affected by the difference in the distributions of the train (source domain) and the test (target domain) sets, e.g., when the sets are produced by different hardware. As a consequence of this
Externí odkaz:
http://arxiv.org/abs/2208.00474
Domain Adaptation (DA) methods are widely used in medical image segmentation tasks to tackle the problem of differently distributed train (source) and test (target) data. We consider the supervised DA task with a limited number of annotated samples f
Externí odkaz:
http://arxiv.org/abs/2107.04914
MRI scans appearance significantly depends on scanning protocols and, consequently, the data-collection institution. These variations between clinical sites result in dramatic drops of CNN segmentation quality on unseen domains. Many of the recently
Externí odkaz:
http://arxiv.org/abs/2008.07357
Autor:
Konstantinos Kamnitsas, Lisa Koch, Mobarakol Islam, Ziyue Xu, Jorge Cardoso, Qi Dou, Nicola Rieke, Sotirios Tsaftaris
This book constitutes the refereed proceedings of the 4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with MICCAI 2022, in September 2022. DART 2022 accepted 13 papers from the 25 submissions rece
Autor:
Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbo
Autor:
Shadi Albarqouni, Spyridon Bakas, Konstantinos Kamnitsas, M. Jorge Cardoso, Bennett Landman, Wenqi Li, Fausto Milletari, Nicola Rieke, Holger Roth, Daguang Xu, Ziyue Xu
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 MICC