Recently, realistic image generation using deep neural networks has become a hot topic in machinelearning and computer vision. Images can be generated at the pixel level by learning from a large collection of images....
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Social media mining has become one of the most popular research areas in Big Data with the explosion of social networking information from Facebook, Twitter, LinkedIn,Weibo and so on. Understanding and representing th...
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The number of international benchmarking competitions is steadily increasing in various fields of machinelearning (ML) research and practice. So far, however, little is known about the common practice as well as bott...
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In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatil-ity in the Chinese market. We have labeled the sentiment of the online financial posts ...
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Stable matching studies how to pair members of two sets with the objective to achieve a matching that satisfies all participating agents based on their preferences. In this research, we consider the case of matching i...
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It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Emotion classification is such an examp...
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Segmentation of nuclei on breast cancer histopathological images is considered a basic and essential step for diagnosis in a computer-aided diagnosis framework. Nuclear segmentation remains a challenging problem due t...
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ISBN:
(纸本)9781479923519
Segmentation of nuclei on breast cancer histopathological images is considered a basic and essential step for diagnosis in a computer-aided diagnosis framework. Nuclear segmentation remains a challenging problem due to the inherent diversity of cancer biology and the variability of the tissue appearance. We present an automatic nuclear segmentation method using an improved hybrid active contour (AC) model driven by both boundary and region information. The initialization of segmentation based on morphological operations and watershed allows for generation of initial closed curves and reduction in computational load of curve evolution for the AC model. Color gradients are computed to capture image gradients along the margin of nucleus. The AC segmentation scheme is performed in a coarse-to-fine fashion which can help to solve the problem of multiple object overlap in an image scene. Segmentation performance was evaluated on various breast cancer histopathological images with different grades and was compared with the existing popular AC models, suggesting that our improved hybrid active contour model can be used to build an accurate and robust nuclear segmentation tool.
We present an automatic breast cancer grading method in histopathological images based on the computer extracted pixel-, object-, and semantic-level features derived from convolutional neural networks (CNN). The multi...
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ISBN:
(纸本)9781479923519
We present an automatic breast cancer grading method in histopathological images based on the computer extracted pixel-, object-, and semantic-level features derived from convolutional neural networks (CNN). The multiple level features allow not only characterization of nuclear polymorphism, but also extraction of structural and interpretable information within the images. In this study, a hybrid level set based segmentation method was used to segment nuclei from the images. A quantile normalization approach was utilized to improve image color consistency. The semantic level features are extracted by a CNN approach, which describe the proportions of nuclei belonging to the different grades, in conjunction with pixel-level (texture) and object-level (structure) features, to form an integrated set of attributes. A support vector machine classifier was trained to discriminate the breast cancer between low, intermediate, and high grades. The results demonstrated that our method achieved accuracy of 0.90 (low vs. high), and 0.74 (low vs. intermediate), and 0.76 (intermediate vs. high), suggesting that the present method could play a fundamental role in developing a computer-aided breast cancer grading system.
In this paper, we present a model to automatically generate efficient transportation networks given a simulated urban environment with predefined population distributions and other physical constraints. Based on the e...
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In this paper, we present a model to automatically generate efficient transportation networks given a simulated urban environment with predefined population distributions and other physical constraints. Based on the e...
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In this paper, we present a model to automatically generate efficient transportation networks given a simulated urban environment with predefined population distributions and other physical constraints. Based on the empirical analysis of different topological structures of networks, we found that that the efficiency of transportation networks heavily depends on the layout of the stations. The model uses the genetic algorithm to optimize the spatial distribution of stations. Then, the Minimum Spanning Tree is constructed upon which extra network routes are built to minimize average travel time within the constraint of a network length. Experimental studies of a simulated Beijing subway system showed that our model can generate a network that is 14% more efficient than the current subway system based on the Beijing population distribution and geography. This study can be extended to other transportation systems designs, and other communication networks.
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