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What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation
Dong JH(董家华)1,2,3; Cong Y(丛杨)1,2; Sun G(孙干)1,2; Zhong BN(钟必能)4; Xu XW(徐晓伟)5
Department机器人学研究室
Conference Name2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Conference DateJune 13-19, 2020
Conference PlaceSeattle, WA, USA
Source Publication2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE
Publication PlaceNew York
2020
Pages4022-4031
Indexed ByEI ; CPCI(ISTP)
EI Accession number20204409431353
WOS IDWOS:000620679504030
Contribution Rank1
ISSN2575-7075
ISBN978-1-7281-7168-5
AbstractUnsupervised domain adaptation has attracted growing research attention on semantic segmentation. However, 1) most existing models cannot be directly applied into lesions transfer of medical images, due to the diverse appearances of same lesion among different datasets; 2) equal attention has been paid into all semantic representations instead of neglecting irrelevant knowledge, which leads to negative transfer of untransferable knowledge. To address these challenges, we develop a new unsupervised semantic transfer model including two complementary modules (i.e., T_D and T_F ) for endoscopic lesions segmentation, which can alternatively determine where and how to explore transferable domain-invariant knowledge between labeled source lesions dataset (e.g., gastroscope) and unlabeled target diseases dataset (e.g., enteroscopy). Specifically, T_D focuses on where to translate transferable visual information of medical lesions via residual transferability-aware bottleneck, while neglecting untransferable visual characterizations. Furthermore, T_F highlights how to augment transferable semantic features of various lesions and automatically ignore untransferable representations, which explores domain-invariant knowledge and in return improves the performance of T_D. To the end, theoretical analysis and extensive experiments on medical endoscopic dataset and several non-medical public datasets well demonstrate the superiority of our proposed model.
Language英语
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/27732
Collection机器人学研究室
Corresponding AuthorCong Y(丛杨)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China
3.University of Chinese Academy of Sciences, Beijing, 100049, China
4.Huaqiao University, Xiamen, Fujian, 361021, China
5.Department of Information Science, University of Arkansas at Little Rock, Arkansas, USA
Recommended Citation
GB/T 7714
Dong JH,Cong Y,Sun G,et al. What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation[C]. New York:IEEE,2020:4022-4031.
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