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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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WRAP-Weakly-supervised-brain-lesion-segmentation-Wen-2019.pdf - Accepted Version - Requires a PDF viewer. Download (906Kb) | Preview |
Official URL: https://doi.org/10.1007/978-3-030-32248-9_24
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) | ||||||
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| Subjects: | Q Science > Q Science (General) R Medicine > RC Internal medicine T Technology > TA Engineering (General). Civil engineering (General) |
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| 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: |
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| 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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