title: Breast tissue image classification based on Semi-supervised Locality Discriminant Projection with Kernels.
abstract: Breast tissue classification is an important and effective way for computer aided diagnosis of breast cancer. We present Semi-supervised Locality Discriminant Projections with Kernels for breast cancer classification. The contributions of this work lie in: 1) Semi-supervised learning is used into Locality Preserving Projections (LPP) to enhance its performance using side-information together with the unlabelled training samples, while current algorithms only consider the side-information but ignoring the unlabeled training samples. 2) Kernel trick is applied into Semi-supervised LPP to improve its ability in the nonlinear classification. 3) The framework of breast cancer classification with Semi-supervised LPP with kernels is presented. Many experiments are implemented on four breast tissue databases to testify and evaluate the feasibility and affectivity of the proposed scheme.
authors: Department of Automatic Test and Control, Harbin Institute of Technology, Harbin, China. junbaolihit@gmail.com
authors: Jun-Bao
authors: JB
authors: Li
authors: Yang
authors: Y
authors: Yu
authors: Zhi-Ming
authors: ZM
authors: Yang
authors: Lin-Lin
authors: LL
authors: Tang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000000981:diagnostic imaging; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D025341:Principal Component Analysis; D011859:Radiography