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Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of DNNs to improve energy efficiency and throughput without...
As a kind of deep learning model, convolutional neural networks (CNNs) have greatly boosted the state-of-the-art performance and have found their successful applications in many fields, such as computer version, pattern recognition, natural language processing, etc. Many distinguished CNN models, for example, AlexNet, Google inception net, VGGNet, and so on, have been developed for various tasks....
Deep Learning (DL), especially Convolutional Neural Networks (CNN), has become the state-of-the-art for a variety of pattern recognition issues. Technological developments have allowed the use of high-end General Purpose Graphic Processor Units (GPGPU) for accelerating numerical problem solving. They resort no only to lower computational time, but also allow considering much larger networks. Hence,...
Super-resolution (SR) of single image is a meaningful challenge in medical images based diagnosis, while the image resolution is limited. Also, numerous deep neural networks based models were proposed and achieve excellent performance which is superior to the previous handcrafted methods. In this paper, we employ a deep convolutional neural networks for the super-resolution (SR) of single medical...
Deep learning architectures based on convolutional neural networks (CNN) are very successful in image recognition tasks. These architectures use a cascade of convolution layers and activation functions. The setup of the number of layers and the number of neurons in each layer, the choice of activation functions and training optimization algorithm are very important. I present GPU implementation of...
Traditional machine learning requires data to be described by attributes prior to applying a learning algorithm. In text classification tasks, many feature engineering methodologies have been proposed to extract meaningful features, however, no best practice approach has emerged. Traditional methods of feature engineering have inherent limitations due to loss of information and the limits of human...
Texture patch classification is an important task in many different computer-aided medical systems. Convolutional Neural Networks (CNN's) have become state-of-the-art for many computer vision tasks in recent years. In this paper, we propose the use of CNN's for the automated classification of colonic mucosa for colon polyp staging in the context of colon cancer screening. This deep learning approach...
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