Journal ArticleDOI
Deep learning
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TLDR
Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.Abstract:
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.read more
Citations
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Journal ArticleDOI
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia
Xi Ouyang,Jiayu Huo,Liming Xia,Fei Shan,Jun Liu,Zhanhao Mo,Fuhua Yan,Zhongxiang Ding,Qi Yang,Bin Song,Feng Shi,Huan Yuan,Ying Wei,Xiaohuan Cao,Yaozong Gao,Dijia Wu,Qian Wang,Dinggang Shen +17 more
TL;DR: Wang et al. as mentioned in this paper developed a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT), and proposed a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses.
Journal ArticleDOI
q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans
Vladimir Golkov,Alexey Dosovitskiy,Jonathan I. Sperl,Marion I. Menzel,Michael Czisch,Philipp G. Sämann,Thomas Brox,Daniel Cremers +7 more
TL;DR: It is demonstrated how deep learning, a group of algorithms based on recent advances in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step and how classical data processing can be streamlined by means of deep learning.
Journal ArticleDOI
Cancer Diagnosis Using Deep Learning: A Bibliographic Review.
TL;DR: This bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements.
Journal ArticleDOI
Deep Spatial–Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting
TL;DR: A novel end-to-end deep learning model, called ST-3DNet, is proposed, which introduces 3D convolutions to automatically capture the correlations of traffic data in both spatial and temporal dimensions and a novel recalibration block is proposed to explicitly quantify the difference of the contributions of the correlations in space.
Journal ArticleDOI
A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises
S. Kevin Zhou,Hayit Greenspan,Christos Davatzikos,James S. Duncan,Bram van Ginneken,Anant Madabhushi,Jerry L. Prince,Daniel Rueckert,Ronald M. Summers +8 more
TL;DR: In this paper, the authors present traits of medical imaging, highlight clinical needs and technical challenges in medical imaging and describe how emerging trends in deep learning are addressing these issues, and conclude with a discussion and presentation of promising future directions.
References
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Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
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Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
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Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.