Konstantinos Kamnitsas | Akateeminen Kirjakauppa

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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 €

DOMAIN ADAPTATION AND REPRESENTATION TRANSFER, AND DISTRIBUTED AND COLLABORATIVE LEARNING - SECOND MICCAI WORKSHOP, DART 2020, A

Shadi Albarqouni; Spyridon Bakas; Konstantinos Kamnitsas; M. Jorge Cardoso; Bennett Landman; Wenqi Li; Fausto Milletari
Springer Nature Switzerland AG (2020)
Pehmeäkantinen kirja

49,60 €

DOMAIN ADAPTATION AND REPRESENTATION TRANSFER, AND AFFORDABLE HEALTHCARE AND AI FOR RESOURCE DIVERSE GLOBAL HEALTH - THIRD MICCA

Shadi Albarqouni; M. Jorge Cardoso; Qi Dou; Konstantinos Kamnitsas; Bishesh Khanal; Islem Rekik; Nicola Rieke; Deb Sheet
Springer Nature Switzerland AG (2021)
Pehmeäkantinen kirja

64,10 €

DOMAIN ADAPTATION AND REPRESENTATION TRANSFER : 5TH MICCAI WORKSHOP, DART 2023, HELD IN CONJUNCTION WITH MICCAI 2023, VANCOUVER,

Lisa Koch (ed.); M. Jorge Cardoso (ed.); Enzo Ferrante (ed.); Konstantinos Kamnitsas (ed.); Mobarakol Islam (ed.); M Jiang
Springer (2023)
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.)

54,40 €

Springer
Sivumäärä: 147 sivua
Asu: Pehmeäkantinen kirja
Julkaisuvuosi: 2022, 20.09.2022 (lisätietoa)
Kieli: Englanti
Tuotesarja: Lecture Notes in Computer Science 13542

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 received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains.

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