Please use this identifier to cite or link to this item:
|Title:||Nasopharyngeal carcinoma lesion extraction using clustering via semi-supervised metric learning with side-information||Authors:||Huang, W.
Magnetic resonance images
Nasopharyngeal carcinoma lesion
Semi-supervised metric learning
|Issue Date:||2008||Citation:||Huang, W., Kap, L.C., Gao, Y., Chong, V. (2008). Nasopharyngeal carcinoma lesion extraction using clustering via semi-supervised metric learning with side-information. IET Conference Publications (543 CP) : 539-543. ScholarBank@NUS Repository. https://doi.org/10.1049/cp:20080373||Abstract:||In this paper, we consider the extraction of nasopharyngeal carcinoma lesion from magnetic resonance images as a clustering problem. The metric used by the clustering algorithm in our proposed method is a new spatially weighted metric, which is learned by semi-supervised metric learning with side-information. Several experiments have been conducted to compare the performance of the proposed metric with similar metrics for the tumor extraction. ©2008 The Institution of Engineering and Technology.||Source Title:||IET Conference Publications||URI:||http://scholarbank.nus.edu.sg/handle/10635/132917||ISBN:||9780863419140||DOI:||10.1049/cp:20080373|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.