Ziyue Xu | Akateeminen Kirjakauppa

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DOMAIN ADAPTATION AND REPRESENTATION TRANSFER AND MEDICAL IMAGE LEARNING WITH LESS LABELS AND IMPERFECT DATA - FIRST MICCAI WORK

Qian Wang; Fausto Milletari; Hien V. Nguyen; Shadi Albarqouni; M. Jorge Cardoso; Nicola Rieke; Ziyue Xu; Konst Kamnitsas
Springer Nature Switzerland AG (2019)
Pehmeäkantinen kirja

49,60 €

DOMAIN ADAPTATION AND REPRESENTATION TRANSFER : 4TH MICCAI WORKSHOP, DART 2022, HELD IN CONJUNCTION WITH MICCAI 2022, SINGAPORE,

Konstantinos Kamnitsas (ed.); Lisa Koch (ed.); Mobarakol Islam (ed.); Ziyue Xu (ed.); Jorge Cardoso (ed.); Qi Dou (ed.)
Springer (2022)
Pehmeäkantinen kirja

54,40 €

MEDICAL OPTICAL IMAGING AND VIRTUAL MICROSCOPY IMAGE ANALYSIS - FIRST INTERNATIONAL WORKSHOP, MOVI 2022, HELD IN CONJUNCTION WIT

Yuankai Huo; Bryan A. Millis; Yuyin Zhou; Xiangxue Wang; Adam P. Harrison; Ziyue Xu
Springer International Publishing AG (2022)
Pehmeäkantinen kirja

54,40 €

FEDERATED LEARNING FOR MEDICAL IMAGING - PRINCIPLES, ALGORITHMS, AND APPLICATIONS

Xiaoxiao Li; Ziyue Xu; Huazhu Fu
Elsevier Science Publishing Co Inc (2025)
Pehmeäkantinen kirja

134,40 €

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data - First MICCAI Work

Qian Wang, Fausto Milletari, Hien V. Nguyen, Shadi Albarqouni, M. Jorge Cardoso, Nicola Rieke, Ziyue Xu, Konst Kamnitsas

49,60 €

Springer Nature Switzerland AG
Sivumäärä: 254 sivua
Asu: Pehmeäkantinen kirja
Painos: 1st ed. 2019
Julkaisuvuosi: 2019, 12.10.2019 (lisätietoa)
Kieli: Englanti
Tuotesarja: Image Processing, Computer Vision, Pattern Recognition, and Graphics

This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.

DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains.

MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection.

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