Knowledge Distillation for Semi-supervised Domain Adaptation

Mauricio Orbes-Arteaga; Jorge Cardoso; Lauge Sørensen; Christian Igel; Sebastien Ourselin; Marc Modat; Mads Nielsen; Akshay Pai

doi:10.1007/978-3-030-32695-1_8

Knowledge Distillation for Semi-supervised Domain Adaptation

Mauricio Orbes-Arteaga^*, Jorge Cardoso, Lauge Sørensen, Christian Igel, Sebastien Ourselin, Marc Modat, Mads Nielsen, Akshay Pai

^*Corresponding author af dette arbejde

6 Citationer (Scopus)

Abstract

In the absence of sufficient data variation (e.g., scanner and protocol variability) in annotated data, deep neural networks (DNNs) tend to overfit during training. As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data. Semi-supervised domain adaptation methods can alleviate this problem by tuning networks to new target domains without the need for annotated data from these domains. Adversarial domain adaptation (ADA) methods are a popular choice that aim to train networks in such a way that the features generated are domain agnostic. However, these methods require careful dataset-specific selection of hyperparameters such as the complexity of the discriminator in order to achieve a reasonable performance. We propose to use knowledge distillation (KD) – an efficient way of transferring knowledge between different DNNs – for semi-supervised domain adaption of DNNs. It does not require dataset-specific hyperparameter tuning, making it generally applicable. The proposed method is compared to ADA for segmentation of white matter hyperintensities (WMH) in magnetic resonance imaging (MRI) scans generated by scanners that are not a part of the training set. Compared with both the baseline DNN (trained on source domain only and without any adaption to target domain) and with using ADA for semi-supervised domain adaptation, the proposed method achieves significantly higher WMH dice scores.

Originalsprog	Engelsk
Titel	OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging - 2nd International Workshop, OR 2.0 2019, and 2nd International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Proceedings
Redaktører	Luping Zhou, Duygu Sarikaya, Seyed Mostafa Kia, Stefanie Speidel, Anand Malpani, Daniel Hashimoto, Mohamad Habes, Tommy Löfstedt, Kerstin Ritter, Hongzhi Wang
Antal sider	9
Forlag	Springer VS
Publikationsdato	1 jan. 2019
Sider	68-76
ISBN (Trykt)	9783030326944
DOI	https://doi.org/10.1007/978-3-030-32695-1_8
Status	Udgivet - 1 jan. 2019
Begivenhed	2nd International Workshop on Context-Aware Surgical Theaters, OR 2.0 2019, and the 2nd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, Kina Varighed: 17 okt. 2019 → 17 okt. 2019

Konference

Konference	2nd International Workshop on Context-Aware Surgical Theaters, OR 2.0 2019, and the 2nd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
Land/Område	Kina
By	Shenzhen
Periode	17/10/2019 → 17/10/2019

Navn	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vol/bind	11796 LNCS
ISSN	0302-9743

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10.1007/978-3-030-32695-1_8

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Orbes-Arteaga, M., Cardoso, J., Sørensen, L., Igel, C., Ourselin, S., Modat, M., Nielsen, M., & Pai, A. (2019). Knowledge Distillation for Semi-supervised Domain Adaptation. I L. Zhou, D. Sarikaya, S. M. Kia, S. Speidel, A. Malpani, D. Hashimoto, M. Habes, T. Löfstedt, K. Ritter, & H. Wang (red.), OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging - 2nd International Workshop, OR 2.0 2019, and 2nd International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Proceedings (s. 68-76). Springer VS. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 11796 LNCS https://doi.org/10.1007/978-3-030-32695-1_8

Knowledge Distillation for Semi-supervised Domain Adaptation. / Orbes-Arteaga, Mauricio; Cardoso, Jorge; Sørensen, Lauge et al.

OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging - 2nd International Workshop, OR 2.0 2019, and 2nd International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Proceedings. red. / Luping Zhou; Duygu Sarikaya; Seyed Mostafa Kia; Stefanie Speidel; Anand Malpani; Daniel Hashimoto; Mohamad Habes; Tommy Löfstedt; Kerstin Ritter; Hongzhi Wang. Springer VS, 2019. s. 68-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11796 LNCS).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

Orbes-Arteaga, M, Cardoso, J, Sørensen, L , Igel, C, Ourselin, S, Modat, M, Nielsen, M & Pai, A 2019, Knowledge Distillation for Semi-supervised Domain Adaptation. i L Zhou, D Sarikaya, SM Kia, S Speidel, A Malpani, D Hashimoto, M Habes, T Löfstedt, K Ritter & H Wang (red), OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging - 2nd International Workshop, OR 2.0 2019, and 2nd International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Proceedings. Springer VS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 11796 LNCS, s. 68-76, 2nd International Workshop on Context-Aware Surgical Theaters, OR 2.0 2019, and the 2nd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019, Shenzhen, Kina, 17/10/2019. https://doi.org/10.1007/978-3-030-32695-1_8

Orbes-Arteaga M, Cardoso J, Sørensen L , Igel C, Ourselin S, Modat M et al. Knowledge Distillation for Semi-supervised Domain Adaptation. I Zhou L, Sarikaya D, Kia SM, Speidel S, Malpani A, Hashimoto D, Habes M, Löfstedt T, Ritter K, Wang H, red., OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging - 2nd International Workshop, OR 2.0 2019, and 2nd International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Proceedings. Springer VS. 2019. s. 68-76. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11796 LNCS). doi: 10.1007/978-3-030-32695-1_8

Orbes-Arteaga, Mauricio ; Cardoso, Jorge ; Sørensen, Lauge et al. / Knowledge Distillation for Semi-supervised Domain Adaptation. OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging - 2nd International Workshop, OR 2.0 2019, and 2nd International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Proceedings. red. / Luping Zhou ; Duygu Sarikaya ; Seyed Mostafa Kia ; Stefanie Speidel ; Anand Malpani ; Daniel Hashimoto ; Mohamad Habes ; Tommy Löfstedt ; Kerstin Ritter ; Hongzhi Wang. Springer VS, 2019. s. 68-76 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11796 LNCS).

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title = "Knowledge Distillation for Semi-supervised Domain Adaptation",

abstract = "In the absence of sufficient data variation (e.g., scanner and protocol variability) in annotated data, deep neural networks (DNNs) tend to overfit during training. As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data. Semi-supervised domain adaptation methods can alleviate this problem by tuning networks to new target domains without the need for annotated data from these domains. Adversarial domain adaptation (ADA) methods are a popular choice that aim to train networks in such a way that the features generated are domain agnostic. However, these methods require careful dataset-specific selection of hyperparameters such as the complexity of the discriminator in order to achieve a reasonable performance. We propose to use knowledge distillation (KD) – an efficient way of transferring knowledge between different DNNs – for semi-supervised domain adaption of DNNs. It does not require dataset-specific hyperparameter tuning, making it generally applicable. The proposed method is compared to ADA for segmentation of white matter hyperintensities (WMH) in magnetic resonance imaging (MRI) scans generated by scanners that are not a part of the training set. Compared with both the baseline DNN (trained on source domain only and without any adaption to target domain) and with using ADA for semi-supervised domain adaptation, the proposed method achieves significantly higher WMH dice scores.",

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AU - Cardoso, Jorge

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AU - Modat, Marc

AU - Nielsen, Mads

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T2 - 2nd International Workshop on Context-Aware Surgical Theaters, OR 2.0 2019, and the 2nd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019

Y2 - 17 October 2019 through 17 October 2019

ER -

Knowledge Distillation for Semi-supervised Domain Adaptation

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