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Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation. / Chen, Shuai; Bortsova, Gerda; Juárez, Antonio García Uceda; van Tulder, Gijs; de Bruijne, Marleen.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. red. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer VS, 2019. s. 457-465 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11766 LNCS).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Chen, S, Bortsova, G, Juárez, AGU, van Tulder, G
& de Bruijne, M 2019,
Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation. i D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (red),
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer VS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), bind 11766 LNCS, s. 457-465, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, Kina,
13/10/2019.
https://doi.org/10.1007/978-3-030-32248-9_51APA
Chen, S., Bortsova, G., Juárez, A. G. U., van Tulder, G.
, & de Bruijne, M. (2019).
Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation. I D. Shen, P-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (red.),
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (s. 457-465). Springer VS. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Bind 11766 LNCS
https://doi.org/10.1007/978-3-030-32248-9_51Vancouver
Chen S, Bortsova G, Juárez AGU, van Tulder G
, de Bruijne M.
Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation. I Shen D, Yap P-T, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, red., Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer VS. 2019. s. 457-465. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11766 LNCS).
https://doi.org/10.1007/978-3-030-32248-9_51Author
Chen, Shuai ; Bortsova, Gerda ; Juárez, Antonio García Uceda ; van Tulder, Gijs ; de Bruijne, Marleen. / Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. red. / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer VS, 2019. s. 457-465 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11766 LNCS).
Bibtex
@inproceedings{174871eb36c24cf199c961d5ff6ed8a4,
title = "Multi-task Attention-Based Semi-supervised Learning for Medical Image Segmentation",
abstract = "We propose a novel semi-supervised image segmentation method that simultaneously optimizes a supervised segmentation and an unsupervised reconstruction objectives. The reconstruction objective uses an attention mechanism that separates the reconstruction of image areas corresponding to different classes. The proposed approach was evaluated on two applications: brain tumor and white matter hyperintensities segmentation. Our method, trained on unlabeled and a small number of labeled images, outperformed supervised CNNs trained with the same number of images and CNNs pre-trained on unlabeled data. In ablation experiments, we observed that the proposed attention mechanism substantially improves segmentation performance. We explore two multi-task training strategies: joint training and alternating training. Alternating training requires fewer hyperparameters and achieves a better, more stable performance than joint training. Finally, we analyze the features learned by different methods and find that the attention mechanism helps to learn more discriminative features in the deeper layers of encoders.",
keywords = "Attention, Brain tumor, Deep learning, Multi-task learning, Segmentation, Semi-supervised learning, White matter hyperintensities",
author = "Shuai Chen and Gerda Bortsova and Ju{\'a}rez, {Antonio Garc{\'i}a Uceda} and {van Tulder}, Gijs and {de Bruijne}, Marleen",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-32248-9_51",
language = "English",
isbn = "9783030322472",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer VS",
pages = "457--465",
editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, {Terry M.} and Ali Khan and Staib, {Lawrence H.} and Caroline Essert and Sean Zhou",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings",
note = "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-task Attention-Based Semi-supervised Learning for Medical Image Segmentation
AU - Chen, Shuai
AU - Bortsova, Gerda
AU - Juárez, Antonio García Uceda
AU - van Tulder, Gijs
AU - de Bruijne, Marleen
PY - 2019/1/1
Y1 - 2019/1/1
N2 - We propose a novel semi-supervised image segmentation method that simultaneously optimizes a supervised segmentation and an unsupervised reconstruction objectives. The reconstruction objective uses an attention mechanism that separates the reconstruction of image areas corresponding to different classes. The proposed approach was evaluated on two applications: brain tumor and white matter hyperintensities segmentation. Our method, trained on unlabeled and a small number of labeled images, outperformed supervised CNNs trained with the same number of images and CNNs pre-trained on unlabeled data. In ablation experiments, we observed that the proposed attention mechanism substantially improves segmentation performance. We explore two multi-task training strategies: joint training and alternating training. Alternating training requires fewer hyperparameters and achieves a better, more stable performance than joint training. Finally, we analyze the features learned by different methods and find that the attention mechanism helps to learn more discriminative features in the deeper layers of encoders.
AB - We propose a novel semi-supervised image segmentation method that simultaneously optimizes a supervised segmentation and an unsupervised reconstruction objectives. The reconstruction objective uses an attention mechanism that separates the reconstruction of image areas corresponding to different classes. The proposed approach was evaluated on two applications: brain tumor and white matter hyperintensities segmentation. Our method, trained on unlabeled and a small number of labeled images, outperformed supervised CNNs trained with the same number of images and CNNs pre-trained on unlabeled data. In ablation experiments, we observed that the proposed attention mechanism substantially improves segmentation performance. We explore two multi-task training strategies: joint training and alternating training. Alternating training requires fewer hyperparameters and achieves a better, more stable performance than joint training. Finally, we analyze the features learned by different methods and find that the attention mechanism helps to learn more discriminative features in the deeper layers of encoders.
KW - Attention
KW - Brain tumor
KW - Deep learning
KW - Multi-task learning
KW - Segmentation
KW - Semi-supervised learning
KW - White matter hyperintensities
U2 - 10.1007/978-3-030-32248-9_51
DO - 10.1007/978-3-030-32248-9_51
M3 - Article in proceedings
AN - SCOPUS:85075682442
SN - 9783030322472
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 457
EP - 465
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
A2 - Shen, Dinggang
A2 - Yap, Pew-Thian
A2 - Liu, Tianming
A2 - Peters, Terry M.
A2 - Khan, Ali
A2 - Staib, Lawrence H.
A2 - Essert, Caroline
A2 - Zhou, Sean
PB - Springer VS
T2 - 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 17 October 2019
ER -
