ShanghaiTech University Knowledge Management System
| Learning Towards Synchronous Network Memorizability and Generalizability for Continual Segmentation Across Multiple Sites | |
| 2022 | |
| Source Publication | LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS)
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| ISSN | 0302-9743 |
| Volume | 13435 LNCS |
| Pages | 380-390 |
| DOI | 10.1007/978-3-031-16443-9_37 |
| Abstract | In clinical practice, a segmentation network is often required to continually learn on a sequential data stream from multiple sites rather than a consolidated set, due to the storage cost and privacy restriction. However, during the continual learning process, existing methods are usually restricted in either network memorizability on previous sites or generalizability on unseen sites. This paper aims to tackle the challenging problem of Synchronous Memorizability and Generalizability (SMG) and to simultaneously improve performance on both previous and unseen sites, with a novel proposed SMG-learning framework. First, we propose a Synchronous Gradient Alignment (SGA) objective, which not only promotes the network memorizability by enforcing coordinated optimization for a small exemplar set from previous sites (called replay buffer), but also enhances the generalizability by facilitating site-invariance under simulated domain shift. Second, to simplify the optimization of SGA objective, we design a Dual-Meta algorithm that approximates the SGA objective as dual meta-objectives for optimization without expensive computation overhead. Third, for efficient rehearsal, we configure the replay buffer comprehensively considering additional inter-site diversity to reduce redundancy. Experiments on prostate MRI data sequentially acquired from six institutes demonstrate that our method can simultaneously achieve higher memorizability and generalizability over state-of-the-art methods. Code is available at https://github.com/jingyzhang/SMG-Learning. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Keyword | Learning systems Medical computing Medical imaging Clinical practices Continual segmentation Data stream Generalizability Learn+ Memorizability Optimisations Sequential data Storage costs Synchronous networks |
| Conference Name | 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 |
| Conference Place | Singapore, Singapore |
| Conference Date | September 18, 2022 - September 22, 2022 |
| URL | 查看原文 |
| Indexed By | EI ; CPCI ; CPCI-S |
| Language | 英语 |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| EI Accession Number | 20224012829537 |
| EI Keywords | Digital storage |
| EISSN | 1611-3349 |
| EI Classification Number | 461.1 Biomedical Engineering ; 722.1 Data Storage, Equipment and Techniques ; 723.5 Computer Applications ; 746 Imaging Techniques |
| Original Document Type | Conference article (CA) |
| Citation statistics | |
| Document Type | 会议论文 |
| Identifier | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/240507 |
| Collection | 生物医学工程学院_PI研究组_沈定刚组 生物医学工程学院_PI研究组_崔智铭组 |
| Corresponding Author | Shen, Dinggang |
| Affiliation | 1.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; 2.School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; 3.Department of Computer Science, The University of Hong Kong, Hong Kong; 4.Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China |
| First Author Affilication | School of Biomedical Engineering,ShanghaiTech University |
| Corresponding Author Affilication | School of Biomedical Engineering,ShanghaiTech University |
| First Signature Affilication | School of Biomedical Engineering,ShanghaiTech University |
| Recommended Citation GB/T 7714 | Zhang, Jingyang,Xue, Peng,Gu, Ran,et al. Learning Towards Synchronous Network Memorizability and Generalizability for Continual Segmentation Across Multiple Sites[C]:Springer Science and Business Media Deutschland GmbH,2022:380-390. |
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