What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation

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	What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation
	Dong JH(董家华)2,3,5; Cong Y(丛杨)2,3; Sun G(孙干)2,3; Zhong BN(钟必能)1; Xu XW(徐晓伟)4
	2020
会议日期	June 13-19, 2020
会议地点	Seattle, WA, USA
页码	4022-4031
英文摘要	Unsupervised 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.
产权排序	1
会议录	2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISSN号	2575-7075
ISBN号	978-1-7281-7168-5
WOS记录号	WOS:000620679504030
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/27732]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Cong Y(丛杨)
作者单位	1.Huaqiao University, Xiamen, Fujian, 361021, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China 4.Department of Information Science, University of Arkansas at Little Rock, Arkansas, USA 5.University of Chinese Academy of Sciences, Beijing, 100049, China
推荐引用方式 GB/T 7714	Dong JH,Cong Y,Sun G,et al. What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation[C]. 见:. Seattle, WA, USA. June 13-19, 2020.