Learn More
Classification plays a critical role in false positive reduction (FPR) in lung nodule computer aided detection (CAD). The difficulty of FPR lies in the variation of the appearances of the nodules, and the imbalance distribution between the nodule and non-nodule class. Moreover, the presence of inherent complex structures in data distribution, such as(More)
Early detection of pulmonary nodules on Multi-Slice Computed Tomography (MSCT) is an important clinical indication for early-stage lung cancer diagnosis. Currently, support vector machines (SVMs) have been widely used in pattern recognition. We are developing a nodule detection system by using the combination of SVMs. For training and testing the SVM(More)
The detection of pulmonary nodules is proven to be of critical importance in early-stage lung cancer diagnosis. Many computer aided detection (CAD) methods combined with morphological approach and pattern recognition technology to identify lung nodules have been proposed to assist the radiologists to improve sensitivity of diagnosis. We present a computer(More)
Component-based software development is becoming a discipline in software engineering community, but it still immature in enterprise application development. OSGi gives some insights for component-based development (CBD). By proposing an ideal component model for enterprise applications, we analyze the performance of OSGi against such a model. The result(More)
BACKGROUND AND PURPOSE The widespread propagation of synchronized neuronal firing in seizure disorders may affect cortical and subcortical brain regions. Diffusion tensor imaging (DTI) can noninvasively quantify white matter integrity. The purpose of this study was to investigate the abnormal changes of white matter in children and adolescents with focal(More)
Computer aided detection (CAD) systems can assist radiologists by offering a second opinion on early diagnosis of lung cancer. Classification and feature representation play critical roles in false-positive reduction (FPR) in lung nodule CAD. We design a deep convolutional neural networks method for nodule classification, which has an advantage of(More)
Class imbalance is one of the challenging problems for machine learning in many real-world applications. Cost-sensitive learning has attracted significant attention in recent years to solve the problem, but it is difficult to determine the precise misclassification costs in practice. There are also other factors that influence the performance of the(More)
As a new approach, grid technology is rapidly used in scientific computing, large-scale data management, and collaborative work. But in the field of manufacturing, the application of grid is just at the beginning. The paper proposes the concept of manufacturing. The needs, definition and architecture of manufacturing gird are discussed, which explains why(More)
We propose a novel two-layer level set approach for segmentation of the left ventricle (LV) from cardiac magnetic resonance (CMR) short-axis images. In our method, endocardium and epicardium are represented by two specified level contours of a level set function. Segmentation of the LV is formulated as a problem of optimizing the level set function such(More)
It has been proven that early detection of pulmonary nodules is an important clinical indication for early-stage lung cancer diagnosis. Recently, support vector machines(SVMs) have been extensively used in pattern recognition. However, the application object for SVMs used for false positives(FPs) reduction when detecting lung nodules is generally based on(More)