Paper Info
Title
A Novel Negative-Transfer-Resistant Fuzzy Clustering Model With a Shared Cross-Domain Transfer Latent Space and its Application to Brain CT Image Segmentation
Abstract
AbstractTraditional 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.
Year
DOI
Venue
2021
10.1109/TCBB.2019.2963873
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Keywords
DocType
Volume
Medical image segmentation, fuzzy clustering, transfer learning, negative transfer
Journal
18
Issue
ISSN
Citations 
1
1545-5963
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Yizhang Jiang138227.24
Xiaoqing Gu2449.30
Dongrui Wu3165893.01
Wenlong Hang400.34
Jing Xue5103.14
Shi Qiu625029.03
Chin-Teng Lin73840392.55