Dexing Kong

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BACKGROUND In recent years, neuroimaging has been increasingly used as an objective method for the diagnosis of Parkinson's disease (PD). Most previous studies were based on invasive imaging modalities or on a single modality which was not an ideal diagnostic tool. In this study, we developed a non-invasive technology intended for use in the diagnosis of(More)
Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous(More)
Interface evolution problems are often solved elegantly by the level set method, which generally requires the time-consuming reinitialization process. In order to avoid reinitialization, we reformulate the vari-ational model as a constrained optimization problem. Then we present an augmented Lagrangian method and a projection Lagrangian method to solve the(More)
PURPOSE To evaluate the accuracy of shear wave elastography (SWE) in the quantitative diagnosis of liver fibrosis severity. METHODS The published literatures were systematically retrieved from PubMed, Embase, Web of science and Scopus up to May 13th, 2016. Included studies reported the pooled sensitivity, specificity, positive and negative predictive(More)
Purpose Segmentation of the liver from abdominal computed tomography (CT) image is an essential step in some computer assisted clinical interventions, such as surgery planning for living donor liver transplant (LDLT), radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment(More)
Example-learning-based algorithms such as those based on sparse coding or neighbor embedding have been popular for single image super-resolution in recent years. However, affected by several critical factors on the training data and example representation, their reconstructions are usually plagued by kinds of artifacts. The removing of these artifacts is(More)
This paper presents a new version of Dropout called Split Dropout (sDropout) and rotational convolution techniques to improve CNNs' performance on image classification. The widely used standard Dropout has advantage of preventing deep neural networks from overfitting by randomly dropping units during training. Our sDropout randomly splits the data into two(More)