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An accurate tumor classification is important to diagnosis and treatment cancers. The conventional methods for tumor classification include training and testing phases, which may cause over fitting. Although this problem can be avoided by using sparse representation classification, the existing sparse representation methods for tumor classification are inefficient. In this paper, an efficient and...
In this paper, we describe a two-stage hybrid approach to select gene features and produce dominant patterns for evaluating the pathological probability. To discover suitable genes as experiment samples for distinguishing the status of gene regulation, we utilized receiver operating characteristic (ROC) method to eliminate non-significant genes of unapparent variation between normal tissues and tumors...
The goal of this paper is to establish the error propagation model of the ultrasound-guided robot for liver cancer coagulation therapy, which consists of ultrasound machine, image-guided software subsystem, position tracking unit and needle-driven robot. The target of tumor is transformed to robot coordinate frame to let the robot move to the target. The transformation includes three dimension ultrasound...
Gene expression data usually contains a large number of genes (several thousand or more) but a small number of samples (usually <100). Among all the genes, many are irrelevant, insignificant or redundant to the discriminant problem under investigation. Hence the identification of informative genes, which have the greatest power for classification, is of fundamental and practical importance to the...
The prostatic adenocarcinoma (ADKP) is the most frequent neoplasy and also the major cause of death for men in United States. Detecting this tumor by human eye from biomedical images is difficult and invasive methods like the prostate needle biopsy are dangerous for the patient. The aim of our research is to develop reliable, non-invasive, computerized methods in order to provide an accurate characterization...
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