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Weakly supervised brain lesion segmentation via attentional representation learning

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Wu, Kai, Du, Bowen, Luo, Man, Wen, Hongkai, Shen, Yiran and Feng, Jianfeng (2019) Weakly supervised brain lesion segmentation via attentional representation learning. In: 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Shenzhen, China, 13-17 Oct 2019. Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, 11766 ISBN 9783030322472. doi:10.1007/978-3-030-32248-9_24

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Official URL: https://doi.org/10.1007/978-3-030-32248-9_24

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Abstract

In this paper, we propose a new weakly supervised 3D brain lesion segmentation approach using attentional representation learning. Our approach only requires image-level labels, and is able to produce accurate segmentation of the 3D lesion volumes. To achieve that, we design a novel dimensional independent attention mechanism on top of the Class Activation Maps (CAMs), which refines the 3D CAMs to obtain better estimates of the lesion volumes, without introducing significantly more trainable variables. The generated attentional CAMs are then used as a source of weak supervision signals to learn a representation model, which can reliably separate the voxels belong to the lesion volumes from those of the normal tissues. The proposed approach has been evaluated on the publicly available BraTS and ISLES datasets. We show with comprehensive experiments that our approach significantly outperforms the competing weakly-supervised methods in both initial lesion localization and the final segmentation, and is able to achieve comparable Dice scores in segmentation comparing to the fully supervised baselines.

Item Type: Conference Item (Paper)
Subjects: Q Science > Q Science (General)
R Medicine > RC Internal medicine
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Diagnostic imaging, Brain -- Imaging, Image segmentation, Machine learning, Three-dimensional imaging
Series Name: Lecture Notes in Computer Science
Journal or Publication Title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Publisher: Springer International Publishing
ISBN: 9783030322472
Book Title: Image Processing, Computer Vision, Pattern Recognition, and Graphics
Official Date: 14 October 2019
Dates:
DateEvent
14 October 2019Available
29 June 2019Accepted
Volume: 11766
DOI: 10.1007/978-3-030-32248-9_24
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): The final authenticated publication is available online at: https://doi.org/10.1007/978-3-030-32248-9_24
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 5 August 2019
Date of first compliant Open Access: 21 October 2019
Conference Paper Type: Paper
Title of Event: 22nd International Conference on Medical Image Computing and Computer Assisted Intervention
Type of Event: Conference
Location of Event: Shenzhen, China
Date(s) of Event: 13-17 Oct 2019
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