AIMC Topic: Neural Networks, Computer

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Stimuli-Aware Visual Emotion Analysis.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Visual emotion analysis (VEA) has attracted great attention recently, due to the increasing tendency of expressing and understanding emotions through images on social networks. Different from traditional vision tasks, VEA is inherently more challengi...

Application of deep learning for image-based Chinese market food nutrients estimation.

Food chemistry
With commercialization of deep learning (DL) models, daily precision dietary record based on images from smartphones becomes possible. This study took advantage of DL techniques on visual recognition tasks and proposed a suite of big-data-driven DL m...

Predicting flocculant dosage in the drinking water treatment process using Elman neural network.

Environmental science and pollution research international
Predicting the flocculant dosage in the drinking water treatment process is essential for public health. However, due to the complexity of water quality and flocculation, many difficulties remain. The present study aimed to report on using artificial...

A multiphase texture-based model of active contours assisted by a convolutional neural network for automatic CT and MRI heart ventricle segmentation.

Computer methods and programs in biomedicine
BACKGROUND: Left and right ventricle automatic segmentation remains one of the more important tasks in computed aided diagnosis. Active contours have shown to be efficient for this task, however they often require user interaction to provide the init...

Improved protein relative solvent accessibility prediction using deep multi-view feature learning framework.

Analytical biochemistry
The accurate prediction of the relative solvent accessibility of a protein is critical to understanding its 3D structure and biological function. In this study, a novel deep multi-view feature learning (DMVFL) framework that integrates three differen...

A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Synthetic magnetic resonance imaging (MRI) is a low cost procedure that serves as a bridge between qualitative and quantitative MRI. However, the proposed methods require very specific sequences or private protocols which ha...

Accurate diagnosis of sepsis using a neural network: Pilot study using routine clinical variables.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Sepsis is a severe infection that increases mortality risk and is one if the main causes of death in intensive care units. Accurate detection is key to successful interventions, but diagnosis of sepsis is complicated becaus...

Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images.

IEEE transactions on medical imaging
Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells react to drug treatment. In this paper, we propose a novel box-based i...

Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks.

IEEE transactions on medical imaging
A variety of deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. Traditional measures of image quality (IQ) have been employed to optimize and evaluate these methods. However, the objective evaluati...

Adaptive Dropout Method Based on Biological Principles.

IEEE transactions on neural networks and learning systems
Dropout is one of the most widely used methods to avoid overfitting neural networks. However, it rigidly and randomly activates neurons according to a fixed probability, which is not consistent with the activation mode of neurons in the human cerebra...