Title | Generalized Brain Image Synthesis with Transferable Convolutional Sparse Coding Networks |
Author | |
Corresponding Author | Feng Zheng; Yefeng Zheng |
DOI | |
Publication Years | 2022-10
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Conference Name | European Conference on Computer Vision2022
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ISSN | 0302-9743
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EISSN | 1611-3349
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ISBN | 978-3-031-19829-8
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Source Title | |
Volume | 13694
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Conference Date | 2022/10/23-2022/10/27
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Conference Place | 特拉维夫
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Publication Place | GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
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Publisher | |
Abstract | High inter-equipment variability and expensive examination costs of brain imaging remain key challenges in leveraging the heterogeneous scans effectively. Despite rapid growth in image-to-image translation with deep learning models, the target brain data may not always be achievable due to the specific attributes of brain imaging. In this paper, we present a novel generalized brain image synthesis method, powered by our transferable convolutional sparse coding networks, to address the lack of interpretable cross-modal medical image representation learning. The proposed approach masters the ability to imitate the machine-like anatomically meaningful imaging by translating features directly under a series of mathematical processings, leading to the reduced domain discrepancy while enhancing model transferability. Specifically, we first embed the globally normalized features into a domain discrepancy metric to learn the domain-invariant representations, then optimally preserve domain-specific geometrical property to reflect the intrinsic graph structures, and further penalize their subspace mismatching to reduce the generalization error. The overall framework is cast in a minimax setting, and the extensive experiments show that the proposed method yields state-of-the-art results on multiple datasets. |
Keywords | |
SUSTech Authorship | Corresponding
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Language | English
|
URL | [Source Record] |
Indexed By | |
WOS Research Area | Computer Science
; Imaging Science & Photographic Technology
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WOS Subject | Computer Science, Artificial Intelligence
; Imaging Science & Photographic Technology
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WOS Accession No | WOS:000903746100011
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Data Source | 人工提交
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Publication Status | 在线出版
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Citation statistics |
Cited Times [WOS]:0
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Document Type | Conference paper |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/415617 |
Department | Southern University of Science and Technology 工学院_计算机科学与工程系 |
Affiliation | 1.Tencent Jarvis Lab, Shenzhen, China 2.Southern University of Science and Technology, China 3.Terminus Group, Beijing, China |
Corresponding Author Affilication | Southern University of Science and Technology |
Recommended Citation GB/T 7714 |
Yawen Huang,Feng Zheng,Xu Sun,et al. Generalized Brain Image Synthesis with Transferable Convolutional Sparse Coding Networks[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2022.
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