Multi-domain adaptation in brain MRI through paired consistency and adversarial learning

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Supervised learning algorithms trained on medical images will often fail to generalize across changes in acquisition parameters. Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain. Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to n target domains (as long as there is paired data covering all domains). Our multi-domain adaptation method utilises a consistency loss combined with adversarial learning. We provide results on white matter lesion hyperintensity segmentation from brain MRIs using the MICCAI 2017 challenge data as the source domain and two target domains. The proposed method significantly outperforms other domain adaptation baselines.

OriginalsprogEngelsk
TitelDomain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings
RedaktørerQian Wang, Fausto Milletari, Nicola Rieke, Hien V. Nguyen, Badri Roysam, Shadi Albarqouni, M. Jorge Cardoso, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le
Antal sider9
ForlagSpringer VS
Publikationsdato2019
Sider54-62
ISBN (Trykt)9783030333904
DOI
StatusUdgivet - 2019
Begivenhed1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019 - Shenzhen, Kina
Varighed: 13 okt. 201917 okt. 2019

Konference

Konference1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019
LandKina
ByShenzhen
Periode13/10/201917/10/2019
NavnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind11795 LNCS
ISSN0302-9743

Links

ID: 231757976