Dazhe Zhao

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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)
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)
To extend the lifetime of the sensor networks as far as possible while maintaining the quality of network coverage is a major concern in the research of coverage control. A systematical analysis on the relationship between the network lifetime and cover sets alternation is given, and by introducing the concept of time weight factor, the network lifetime(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)
Class imbalance is one of the challenging problems for machine learning in many real-world applications. Other issues, such as within-class imbalance and high dimensionality, can exacerbate the problem. We propose a method HPS-DRS that combines two ideas: Hybrid Probabilistic Sampling technique ensemble with Diverse Random Subspace to address these issues.(More)
Multi-channel communication in a Wireless Mesh Network with routers having multiple radio interfaces significantly enhances the network capacity. Efficient channel assignment is critical for realization of optimal throughput in such networks. In this paper, we investigate the problem of finding the largest number of links that can be connected with the(More)
Learning from imbalanced data is an important and common problem. Many methods have been proposed to address and attempt to solve the problem, including sampling and cost-sensitive learning. This paper presents an effective wrapper approach incorporating the evaluation measure directly into the objective function of cost-sensitive neural network to improve(More)