AIMC Topic: Neural Networks, Computer

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Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer.

Journal of digital imaging
Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural net...

Pre-trained language model augmented adversarial training network for Chinese clinical event detection.

Mathematical biosciences and engineering : MBE
Clinical event detection (CED) is a hot topic and essential task in medical artificial intelligence, which has attracted the attention from academia and industry over the recent years. However, most studies focus on English clinical narratives. Owing...

STDP Forms Associations between Memory Traces in Networks of Spiking Neurons.

Cerebral cortex (New York, N.Y. : 1991)
Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. Ho...

Geometry of Energy Landscapes and the Optimizability of Deep Neural Networks.

Physical review letters
Deep neural networks are workhorse models in machine learning with multiple layers of nonlinear functions composed in series. Their loss function is highly nonconvex, yet empirically even gradient descent minimization is sufficient to arrive at accur...

Computer-assisted Diagnosis of Breast Cancer by Cell Network Matrix Extraction and Multilayer Perceptron Analysis.

Annals of clinical and laboratory science
OBJECTIVE: Diagnosis of breast cancer is based on identification of various morphologic features by histopathologic examination of the specimen. Ancillary immunohistochemical and molecular analyses provide additional information that is prognostic an...

A feedforward neural network for direction-of-arrival estimation.

The Journal of the Acoustical Society of America
This paper examines the relationship between conventional beamforming and linear supervised learning, then develops a nonlinear deep feed-forward neural network (FNN) for direction-of-arrival (DOA) estimation. First, conventional beamforming is refor...

Spectral forecast: A general purpose prediction model as an alternative to classical neural networks.

Chaos (Woodbury, N.Y.)
Here, we describe a general-purpose prediction model. Our approach requires three matrices of equal size and uses two equations to determine the behavior against two possible outcomes. We use an example based on photon-pixel coupling data to show tha...

Dynamic behaviors of hyperbolic-type memristor-based Hopfield neural network considering synaptic crosstalk.

Chaos (Woodbury, N.Y.)
Crosstalk phenomena taking place between synapses can influence signal transmission and, in some cases, brain functions. It is thus important to discover the dynamic behaviors of the neural network infected by synaptic crosstalk. To achieve this, in ...

Temporal convolutional networks allow early prediction of events in critical care.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Clinical interventions and death in the intensive care unit (ICU) depend on complex patterns in patients' longitudinal data. We aim to anticipate these events earlier and more consistently so that staff can consider preemptive action.

Generalized Born radii computation using linear models and neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of generalized Born (GB) radii, which is accomplished by ...