AIMC Topic: Neural Networks, Computer

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Learning the implicit strain reconstruction in ultrasound elastography using privileged information.

Medical image analysis
Quasi-static ultrasound elastography is an importance imaging technology to assess the conditions of various diseases through reconstructing the tissue strain from radio frequency data. State-of-the-art strain reconstruction techniques suffer from th...

Bifurcation structure determines different phase-amplitude coupling patterns in the activity of biologically plausible neural networks.

NeuroImage
Phase-amplitude cross frequency coupling (PAC) is a rather ubiquitous phenomenon that has been observed in a variety of physical domains; however, the mechanisms underlying the emergence of PAC and its functional significance in the context of neural...

Machine Learning Approaches for Automated Lesion Detection in Microwave Breast Imaging Clinical Data.

Scientific reports
Breast lesion detection employing state of the art microwave systems provide a safe, non-ionizing technique that can differentiate healthy and non-healthy tissues by exploiting their dielectric properties. In this paper, a microwave apparatus for bre...

Wx: a neural network-based feature selection algorithm for transcriptomic data.

Scientific reports
Next-generation sequencing (NGS), which allows the simultaneous sequencing of billions of DNA fragments simultaneously, has revolutionized how we study genomics and molecular biology by generating genome-wide molecular maps of molecules of interest. ...

Visualizing complex feature interactions and feature sharing in genomic deep neural networks.

BMC bioinformatics
BACKGROUND: Visualization tools for deep learning models typically focus on discovering key input features without considering how such low level features are combined in intermediate layers to make decisions. Moreover, many of these methods examine ...

Adaptive Neural Event-Triggered Control for Discrete-Time Strict-Feedback Nonlinear Systems.

IEEE transactions on cybernetics
This paper proposes a novel event-triggered (ET) adaptive neural control scheme for a class of discrete-time nonlinear systems in a strict-feedback form. In the proposed scheme, the ideal control input is derived in a recursive design process, which ...

k-Space deep learning for reference-free EPI ghost correction.

Magnetic resonance in medicine
PURPOSE: Nyquist ghost artifacts in echo planar imaging (EPI) are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field ...

Using artificial neural network in determining postharvest LIFE of kiwifruit.

Journal of the science of food and agriculture
BACKGROUND: Artificial intelligence systems have been employed for the development of predictive models that estimate many agricultural processes.