J Med Syst - Breast tissue image classification based on Semi-supervised Locality Discriminant Projection with Kernels.

Tópicos

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{ data(3963) clinic(1234) research(1004) }
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{ time(1939) patient(1703) rate(768) }
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Resumo

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.

Resumo Limpo

breast tissu classif import effect way comput aid diagnosi breast cancer present semisupervis local discrimin project kernel breast cancer classif contribut work lie semisupervis learn use local preserv project lpp enhanc perform use sideinform togeth unlabel train sampl current algorithm consid sideinform ignor unlabel train sampl kernel trick appli semisupervis lpp improv abil nonlinear classif framework breast cancer classif semisupervis lpp kernel present mani experi implement four breast tissu databas testifi evalu feasibl affect propos scheme

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