AI Medical Compendium Topic:
Signal-To-Noise Ratio

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Design, verification and robotic application of a novel recurrent neural network for computing dynamic Sylvester equation.

Neural networks : the official journal of the International Neural Network Society
To solve dynamic Sylvester equation in the presence of additive noises, a novel recurrent neural network (NRNN) with finite-time convergence and excellent robustness is proposed and analyzed in this paper. As compared with the design process of Zhang...

Automated chest screening based on a hybrid model of transfer learning and convolutional sparse denoising autoencoder.

Biomedical engineering online
OBJECTIVE: In this paper, we aim to investigate the effect of computer-aided triage system, which is implemented for the health checkup of lung lesions involving tens of thousands of chest X-rays (CXRs) that are required for diagnosis. Therefore, hig...

Assessment of the generalization of learned image reconstruction and the potential for transfer learning.

Magnetic resonance in medicine
PURPOSE: Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to asse...

Denoising Autoencoder Self-Organizing Map (DASOM).

Neural networks : the official journal of the International Neural Network Society
In this report, we address the question of combining nonlinearities of neurons into networks for modeling increasingly varying and progressively more complex functions. A fundamental approach is the use of higher-level representations devised by rest...

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network.

Sensors (Basel, Switzerland)
As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. However, there are still two probl...

KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images.

Magnetic resonance in medicine
PURPOSE: To demonstrate accurate MR image reconstruction from undersampled k-space data using cross-domain convolutional neural networks (CNNs) METHODS: Cross-domain CNNs consist of 3 components: (1) a deep CNN operating on the k-space (KCNN), (2) a ...

Super-resolution musculoskeletal MRI using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods.

The design and validation of a hybrid digital-signal-processing plug-in for traditional cochlear implant speech processors.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Cochlear implants (CIs) are electronic devices restoring partial hearing to deaf individuals with profound hearing loss. In this paper, a new plug-in for traditional IIR filter-banks (FBs) is presented for cochlear implants ...

Non-monotonic convergence of online learning algorithms for perceptrons with noisy teacher.

Neural networks : the official journal of the International Neural Network Society
Learning curves of simple perceptron were derived here. The learning curve of the perceptron learning with noisy teacher was shown to be non-monotonic, which has never appeared even though the learning curves have been analyzed for half a century. In...