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Multi-domain adaptation in brain MRI through paired consistency and adversarial learning. / Orbes-Arteaga, Mauricio; Varsavsky, Thomas; Sudre, Carole H.; Eaton-Rosen, Zach; Haddow, Lewis J.; Sørensen, Lauge; Nielsen, Mads; Pai, Akshay; Ourselin, Sébastien; Modat, Marc; Nachev, Parashkev; Cardoso, M. Jorge.
Domain 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. red. / Qian 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. Springer VS, 2019. s. 54-62 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11795 LNCS).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Orbes-Arteaga, M, Varsavsky, T, Sudre, CH, Eaton-Rosen, Z, Haddow, LJ, Sørensen, L
, Nielsen, M, Pai, A, Ourselin, S, Modat, M, Nachev, P & Cardoso, MJ 2019,
Multi-domain adaptation in brain MRI through paired consistency and adversarial learning. i Q Wang, F Milletari, N Rieke, HV Nguyen, B Roysam, S Albarqouni, MJ Cardoso, Z Xu, K Kamnitsas, V Patel, S Jiang, K Zhou, K Luu & N Le (red),
Domain 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. Springer VS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 11795 LNCS, s. 54-62, 1st 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,
13/10/2019.
https://doi.org/10.1007/978-3-030-33391-1_7APA
Orbes-Arteaga, M., Varsavsky, T., Sudre, C. H., Eaton-Rosen, Z., Haddow, L. J., Sørensen, L.
, Nielsen, M., Pai, A., Ourselin, S., Modat, M., Nachev, P., & Cardoso, M. J. (2019).
Multi-domain adaptation in brain MRI through paired consistency and adversarial learning. I Q. Wang, F. Milletari, N. Rieke, H. V. Nguyen, B. Roysam, S. Albarqouni, M. J. Cardoso, Z. Xu, K. Kamnitsas, V. Patel, S. Jiang, K. Zhou, K. Luu, & N. Le (red.),
Domain 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 (s. 54-62). Springer VS. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 11795 LNCS
https://doi.org/10.1007/978-3-030-33391-1_7Vancouver
Orbes-Arteaga M, Varsavsky T, Sudre CH, Eaton-Rosen Z, Haddow LJ, Sørensen L o.a.
Multi-domain adaptation in brain MRI through paired consistency and adversarial learning. I Wang Q, Milletari F, Rieke N, Nguyen HV, Roysam B, Albarqouni S, Cardoso MJ, Xu Z, Kamnitsas K, Patel V, Jiang S, Zhou K, Luu K, Le N, red., Domain 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. Springer VS. 2019. s. 54-62. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11795 LNCS).
https://doi.org/10.1007/978-3-030-33391-1_7Author
Orbes-Arteaga, Mauricio ; Varsavsky, Thomas ; Sudre, Carole H. ; Eaton-Rosen, Zach ; Haddow, Lewis J. ; Sørensen, Lauge ; Nielsen, Mads ; Pai, Akshay ; Ourselin, Sébastien ; Modat, Marc ; Nachev, Parashkev ; Cardoso, M. Jorge. / Multi-domain adaptation in brain MRI through paired consistency and adversarial learning. Domain 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. red. / Qian 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. Springer VS, 2019. s. 54-62 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11795 LNCS).
Bibtex
@inproceedings{c36f80e76d03495c9475d70e882d73d2,
title = "Multi-domain adaptation in brain MRI through paired consistency and adversarial learning",
abstract = "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.",
keywords = "Adversarial learning, Brain MR, Domain adaptation",
author = "Mauricio Orbes-Arteaga and Thomas Varsavsky and Sudre, {Carole H.} and Zach Eaton-Rosen and Haddow, {Lewis J.} and Lauge S{\o}rensen and Mads Nielsen and Akshay Pai and S{\'e}bastien Ourselin and Marc Modat and Parashkev Nachev and Cardoso, {M. Jorge}",
year = "2019",
doi = "10.1007/978-3-030-33391-1_7",
language = "English",
isbn = "9783030333904",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer VS",
pages = "54--62",
editor = "Qian Wang and Fausto Milletari and Nicola Rieke and Nguyen, {Hien V.} and Badri Roysam and Shadi Albarqouni and Cardoso, {M. Jorge} and Ziyue Xu and Konstantinos Kamnitsas and Vishal Patel and Steve Jiang and Kevin Zhou and Khoa Luu and Ngan Le",
booktitle = "Domain 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",
note = "1st 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 ; Conference date: 13-10-2019 Through 17-10-2019",
}
RIS
TY - GEN
T1 - Multi-domain adaptation in brain MRI through paired consistency and adversarial learning
AU - Orbes-Arteaga, Mauricio
AU - Varsavsky, Thomas
AU - Sudre, Carole H.
AU - Eaton-Rosen, Zach
AU - Haddow, Lewis J.
AU - Sørensen, Lauge
AU - Nielsen, Mads
AU - Pai, Akshay
AU - Ourselin, Sébastien
AU - Modat, Marc
AU - Nachev, Parashkev
AU - Cardoso, M. Jorge
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Adversarial learning
KW - Brain MR
KW - Domain adaptation
U2 - 10.1007/978-3-030-33391-1_7
DO - 10.1007/978-3-030-33391-1_7
M3 - Article in proceedings
AN - SCOPUS:85075681547
SN - 9783030333904
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 54
EP - 62
BT - Domain 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
A2 - Wang, Qian
A2 - Milletari, Fausto
A2 - Rieke, Nicola
A2 - Nguyen, Hien V.
A2 - Roysam, Badri
A2 - Albarqouni, Shadi
A2 - Cardoso, M. Jorge
A2 - Xu, Ziyue
A2 - Kamnitsas, Konstantinos
A2 - Patel, Vishal
A2 - Jiang, Steve
A2 - Zhou, Kevin
A2 - Luu, Khoa
A2 - Le, Ngan
PB - Springer VS
T2 - 1st 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
Y2 - 13 October 2019 through 17 October 2019
ER -
