AIMC Topic: Neural Networks, Computer

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Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.

PLoS computational biology
Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically...

Exploring Duality in Visual Question-Driven Top-Down Saliency.

IEEE transactions on neural networks and learning systems
Top-down, goal-driven visual saliency exerts a huge influence on the human visual system for performing visual tasks. Text generations, like visual question answering (VQA) and visual question generation (VQG), have intrinsic connections with top-dow...

Hierarchical gated recurrent neural network with adversarial and virtual adversarial training on text classification.

Neural networks : the official journal of the International Neural Network Society
Document classification aims to assign one or more classes to a document for ease of management by understanding the content of a document. Hierarchical attention network (HAN) has been showed effective to classify documents that are ambiguous. HAN p...

A dynamic neural network model for predicting risk of Zika in real time.

BMC medicine
BACKGROUND: In 2015, the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreak's...

Generating Point Cloud from Measurements and Shapes Based on Convolutional Neural Network: An Application for Building 3D Human Model.

Computational intelligence and neuroscience
It has been widely known that 3D shape models are comprehensively parameterized using point cloud and meshes. The point cloud particularly is much simpler to handle compared with meshes, and it also contains the shape information of a 3D model. In th...

Generative adversarial network in medical imaging: A review.

Medical image analysis
Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminato...

Reference Trajectory Reshaping Optimization and Control of Robotic Exoskeletons for Human-Robot Co-Manipulation.

IEEE transactions on cybernetics
For human-robot co-manipulation by robotic exoskeletons, the interaction forces provide a communication channel through which the human and the robot can coordinate their actions. In this article, an optimization approach for reshaping the physical i...

Getting to Know Your Neighbor: Protein Structure Prediction Comes of Age with Contextual Machine Learning.

Journal of computational biology : a journal of computational molecular cell biology
The folding of a protein structure is a process governed by both local and nonlocal interactions. While incorporating local dependencies into a machine learning algorithm for protein structure prediction is simple and has been exploited for some time...

A data augmentation approach to train fully convolutional networks for left ventricle segmentation.

Magnetic resonance imaging
Left ventricle (LV) segmentation plays an important role in the diagnosis of cardiovascular diseases. The cardiac contractile function can be quantified by measuring the segmentation results of LVs. Fully convolutional networks (FCNs) have been prove...

Efficient IntVec: High recognition rate with reduced computational cost.

Neural networks : the official journal of the International Neural Network Society
In many deep neural networks for pattern recognition, the input pattern is classified in the deepest layer based on features extracted through intermediate layers. IntVec (interpolating-vector) is known to be a powerful method for this process of cla...