AIMC Topic: Neural Networks, Computer

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Path Generator with Unpaired Samples Employing Generative Adversarial Networks.

Sensors (Basel, Switzerland)
Interactive technologies such as augmented reality have grown in popularity, but specialized sensors and high computer power must be used to perceive and analyze the environment in order to obtain an immersive experience in real time. However, these ...

GPS Spoofing Detection Method for Small UAVs Using 1D Convolution Neural Network.

Sensors (Basel, Switzerland)
The navigation of small unmanned aerial vehicles (UAVs), such as quadcopters, significantly relies on the global positioning system (GPS); however, UAVs are vulnerable to GPS spoofing attacks. GPS spoofing is an attempt to manipulate a GPS receiver b...

N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning.

Scientific data
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain. However, the current artificial intelligence systems meet difficulties in achieving this ability. Similar challenges also exist for b...

A faster way to model neuronal circuitry.

eLife
Artificial neural networks could pave the way for efficiently simulating large-scale models of neuronal networks in the nervous system.

Identify Representative Samples by Conditional Random Field of Cancer Histology Images.

IEEE transactions on medical imaging
Pathology analysis is crucial to precise cancer diagnoses and the succeeding treatment plan as well. To detect abnormality in histopathology images with prevailing patch-based convolutional neural networks (CNNs), contextual information often serves ...

Prior Attention Network for Multi-Lesion Segmentation in Medical Images.

IEEE transactions on medical imaging
The accurate segmentation of multiple types of lesions from adjacent tissues in medical images is significant in clinical practice. Convolutional neural networks (CNNs) based on the coarse-to-fine strategy have been widely used in this field. However...

Using Simulated Training Data of Voxel-Level Generative Models to Improve 3D Neuron Reconstruction.

IEEE transactions on medical imaging
Reconstructing neuron morphologies from fluorescence microscope images plays a critical role in neuroscience studies. It relies on image segmentation to produce initial masks either for further processing or final results to represent neuronal morpho...

Respiratory motion prediction based on deep artificial neural networks in CyberKnife system: A comparative study.

Journal of applied clinical medical physics
BACKGROUND: In external beam radiotherapy, a prediction model is required to compensate for the temporal system latency that affects the accuracy of radiation dose delivery. This study focused on a thorough comparison of seven deep artificial neural ...

Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Age-related macular degeneration (AMD) is a degenerative disorder affecting the macula, a key area of the retina for visual acuity. Nowadays, AMD is the most frequent cause of blindness in developed countries. Although some...

Spatio-temporally smoothed deep survival neural network.

Journal of biomedical informatics
The analysis of registry data has important implications for cancer monitoring, control, and treatment. In such analysis, (semi)parametric models, such as the Cox Proportional Hazards model, have been routinely adopted. In recent years, deep neural n...