AIMC Topic: Neural Networks, Computer

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Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging.

NeuroImage. Clinical
Dopaminergic degeneration is a pathologic hallmark of Parkinson's disease (PD), which can be assessed by dopamine transporter imaging such as FP-CIT SPECT. Until now, imaging has been routinely interpreted by human though it can show interobserver va...

Mittag-Leffler synchronization of fractional neural networks with time-varying delays and reaction-diffusion terms using impulsive and linear controllers.

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose a fractional-order neural network system with time-varying delays and reaction-diffusion terms. We first develop a new Mittag-Leffler synchronization strategy for the controlled nodes via impulsive controllers. Using the fra...

Computer-assisted cytologic diagnosis in pancreatic FNA: An application of neural networks to image analysis.

Cancer cytopathology
BACKGROUND: Fine-needle aspiration (FNA) biopsy is an accurate method for the diagnosis of solid pancreatic masses. However, a significant number of cases still pose a diagnostic challenge. The authors have attempted to design a computer model to aid...

Automated arteriole and venule classification using deep learning for retinal images from the UK Biobank cohort.

Computers in biology and medicine
The morphometric characteristics of the retinal vasculature are associated with future risk of many systemic and vascular diseases. However, analysis of data from large population based studies is needed to help resolve uncertainties in some of these...

Network Location-Aware Service Recommendation with Random Walk in Cyber-Physical Systems.

Sensors (Basel, Switzerland)
Cyber-physical systems (CPS) have received much attention from both academia and industry. An increasing number of functions in CPS are provided in the way of services, which gives rise to an urgent task, that is, how to recommend the suitable servic...

ASD+M: Automatic parameter tuning in stochastic optimization and on-line learning.

Neural networks : the official journal of the International Neural Network Society
In this paper the classic momentum algorithm for stochastic optimization is considered. A method is introduced that adjusts coefficients for this algorithm during its operation. The method does not depend on any preliminary knowledge of the optimizat...

Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes.

Current biology : CB
Even with great advances in machine vision, animals are still unmatched in their ability to visually search complex scenes. Animals from bees [1, 2] to birds [3] to humans [4-12] learn about the statistical relations in visual environments to guide a...

Analysis of deep learning methods for blind protein contact prediction in CASP12.

Proteins
Here we present the results of protein contact prediction achieved in CASP12 by our RaptorX-Contact server, which is an early implementation of our deep learning method for contact prediction. On a set of 38 free-modeling target domains with a median...

Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes.

Clinical chemistry
BACKGROUND: Morphologic profiling of the erythrocyte population is a widely used and clinically valuable diagnostic modality, but one that relies on a slow manual process associated with significant labor cost and limited reproducibility. Automated p...

Reconstructing cell cycle and disease progression using deep learning.

Nature communications
We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progressi...