AIMC Topic: Neural Networks, Computer

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Deep neural network-estimated age using optical coherence tomography predicts mortality.

GeroScience
The concept of biological age has emerged as a measurement that reflects physiological and functional decline with ageing. Here we aimed to develop a deep neural network (DNN) model that predicts biological age from optical coherence tomography (OCT)...

Diagnosis of diabetes mellitus using high frequency ultrasound and convolutional neural network.

Ultrasonics
The incidence of diabetes mellitus has been increasing, prompting the search for non-invasive diagnostic methods. Although current methods exist, these have certain limitations, such as low reliability and accuracy, difficulty in individual patient a...

Adaptive machine learning method for photoacoustic computed tomography based on sparse array sensor data.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Photoacoustic computed tomography (PACT) is a non-invasive biomedical imaging technology that has developed rapidly in recent decades, especially has shown potential for small animal studies and early diagnosis of human dise...

Lower and upper bounds for numbers of linear regions of graph convolutional networks.

Neural networks : the official journal of the International Neural Network Society
Graph neural networks (GNNs) have become a popular choice for analyzing graph data in the last few years, and characterizing their expressiveness has become an active area of research. One popular measure of expressiveness is the number of linear reg...

Self-supervised depth super-resolution with contrastive multiview pre-training.

Neural networks : the official journal of the International Neural Network Society
Many low-level vision tasks, including guided depth super-resolution (GDSR), struggle with the issue of insufficient paired training data. Self-supervised learning is a promising solution, but it remains challenging to upsample depth maps without the...

Efficient and accurate large library ligand docking with KarmaDock.

Nature computational science
Ligand docking is one of the core technologies in structure-based virtual screening for drug discovery. However, conventional docking tools and existing deep learning tools may suffer from limited performance in terms of speed, pose quality and bindi...

Specialist hybrid models with asymmetric training for malaria prevalence prediction.

Frontiers in public health
Malaria is a common and serious disease that primarily affects developing countries and its spread is influenced by a variety of environmental and human behavioral factors; therefore, accurate prevalence prediction has been identified as a critical c...

Oral mucosal disease recognition based on dynamic self-attention and feature discriminant loss.

Oral diseases
OBJECTIVES: To develop a dynamic self-attention and feature discrimination loss function (DSDF) model for identifying oral mucosal diseases presented to solve the problems of data imbalance, complex image background, and high similarity and differenc...

Exploring the role of texture features in deep convolutional neural networks: Insights from Portilla-Simoncelli statistics.

Neural networks : the official journal of the International Neural Network Society
It is well-understood that the performance of Deep Convolutional Neural Networks (DCNNs) in image recognition tasks is influenced not only by shape but also by texture information. Despite this, understanding the internal representations of DCNNs rem...

Synergetic learning for unknown nonlinear H control using neural networks.

Neural networks : the official journal of the International Neural Network Society
The well-known H control design gives robustness to a controller by rejecting perturbations from the external environment, which is difficult to do for completely unknown affine nonlinear systems. Accordingly, the immediate objective of this paper is...