A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation | |
Jiang, Yizhang8,9; Gu, Xiaoqing7; Wu, Dongrui6; Hang, Wenlong4,5; Xue, Jing3; Qiu, Shi2; Lin, Chin-Teng1 | |
刊名 | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS |
2021 | |
卷号 | 18期号:1页码:40-52 |
关键词 | Medical image segmentation fuzzy clustering transfer learning negative transfer |
ISSN号 | 1545-5963;1557-9964 |
DOI | 10.1109/TCBB.2019.2963873 |
产权排序 | 8 |
英文摘要 | Traditional clustering algorithms for medical image segmentation can only achieve satisfactory clustering performance under relatively ideal conditions, in which there is adequate data from the same distribution, and the data is rarely disturbed by noise or outliers. However, a sufficient amount of medical images with representative manual labels are often not available, because medical images are frequently acquired with different scanners (or different scan protocols) or polluted by various noises. Transfer learning improves learning in the target domain by leveraging knowledge from related domains. Given some target data, the performance of transfer learning is determined by the degree of relevance between the source and target domains. To achieve positive transfer and avoid negative transfer, a negative-transfer-resistant mechanism is proposed by computing the weight of transferred knowledge. Extracting a negative-transfer-resistant fuzzy clustering model with a shared cross-domain transfer latent space (called NTR-FC-SCT) is proposed by integrating negative-transfer-resistant and maximum mean discrepancy (MMD) into the framework of fuzzy c-means clustering. Experimental results show that the proposed NTR-FC-SCT model outperformed several traditional non-transfer and related transfer clustering algorithms. |
语种 | 英语 |
出版者 | IEEE COMPUTER SOC |
WOS记录号 | WOS:000615042600005 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/94521] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Gu, Xiaoqing |
作者单位 | 1.Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Artificial Intelligence, Ultimo, NSW 2007, Australia 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian 710119, Shaanxi, Peoples R China 3.Nanjing Med Univ, Dept Nephrol, Affiliated Wuxi Peoples Hosp, Wuxi 214023, Jiangsu, Peoples R China 4.Nanjing Med Univ, Sch Comp Sci & Technol, Wuxi 214023, Jiangsu, Peoples R China 5.Nanjing Tech Univ, Sch Comp Sci & Technol, Nanjing 211816, Jiangsu, Peoples R China 6. Huazhong Univ Sci & Technol, Sch Automat, Key Lab, Minist Educ Image Proc & Intelligent Control, Wuhan 430074, Hubei, Peoples R China 7.Changzhou Univ, Sch Informat Sci & Engn, Changzhou 213164, Jiangsu, Peoples R China 8.Jiangnan Univ, Sch Digital Media, Wuxi 214122, Jiangsu, Peoples R China 9.Jiangnan Univ, Jiangsu Key Lab Media Design & Software Technol, Wuxi 214122, Jiangsu, Peoples R China |
推荐引用方式 GB/T 7714 | Jiang, Yizhang,Gu, Xiaoqing,Wu, Dongrui,et al. A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2021,18(1):40-52. |
APA | Jiang, Yizhang.,Gu, Xiaoqing.,Wu, Dongrui.,Hang, Wenlong.,Xue, Jing.,...&Lin, Chin-Teng.(2021).A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,18(1),40-52. |
MLA | Jiang, Yizhang,et al."A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 18.1(2021):40-52. |
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