中文版 | English
Title

Generalized Brain Image Synthesis with Transferable Convolutional Sparse Coding Networks

Author
Corresponding AuthorFeng Zheng; Yefeng Zheng
DOI
Publication Years
2022-10
Conference Name
European Conference on Computer Vision2022
ISSN
0302-9743
EISSN
1611-3349
ISBN
978-3-031-19829-8
Source Title
Volume
13694
Conference Date
2022/10/23-2022/10/27
Conference Place
特拉维夫
Publication Place
GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
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
Language
English
URL[Source Record]
Indexed By
WOS Research Area
Computer Science ; Imaging Science & Photographic Technology
WOS Subject
Computer Science, Artificial Intelligence ; Imaging Science & Photographic Technology
WOS Accession No
WOS:000903746100011
Data Source
人工提交
Publication Status
在线出版
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/415617
DepartmentSouthern 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 AffilicationSouthern 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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