AIMC Topic: Neural Networks, Computer

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Contrastive Learning for Image Registration in Visual Teach and Repeat Navigation.

Sensors (Basel, Switzerland)
Visual teach and repeat navigation (VT&R) is popular in robotics thanks to its simplicity and versatility. It enables mobile robots equipped with a camera to traverse learned paths without the need to create globally consistent metric maps. Although ...

State-of-the-art retinal vessel segmentation with minimalistic models.

Scientific reports
The segmentation of retinal vasculature from eye fundus images is a fundamental task in retinal image analysis. Over recent years, increasingly complex approaches based on sophisticated Convolutional Neural Network architectures have been pushing per...

A generative adversarial network for synthetization of regions of interest based on digital mammograms.

Scientific reports
Deep learning (DL) models are becoming pervasive and applicable to computer vision, image processing, and synthesis problems. The performance of these models is often improved through architectural configuration, tweaks, the use of enormous training ...

Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images.

Nature communications
With age, the prevalence of diseases such as fatty liver disease, cirrhosis, and type two diabetes increases. Approaches to both predict abdominal age and identify risk factors for accelerated abdominal age may ultimately lead to advances that will d...

Transfer learning for medical image classification: a literature review.

BMC medical imaging
BACKGROUND: Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcom...

Brain-inspired computing needs a master plan.

Nature
New computing technologies inspired by the brain promise fundamentally different ways to process information with extreme energy efficiency and the ability to handle the avalanche of unstructured and noisy data that we are generating at an ever-incre...

Research on Disease Prediction Method Based on R-Lookahead-LSTM.

Computational intelligence and neuroscience
Cardiovascular disease is one of the most serious diseases that threaten human health in the world today. Therefore, establishing a high-quality disease prediction model is of great significance for the prevention and treatment of cardiovascular dise...

Personality Privacy Protection Method of Social Users Based on Generative Adversarial Networks.

Computational intelligence and neuroscience
Obscuring or otherwise minimizing the release of personality information from potential victims of social engineering attacks effectively interferes with an attacker's personality analysis and reduces the success rate of social engineering attacks. W...

Network Intrusion Detection Method Based on FCWGAN and BiLSTM.

Computational intelligence and neuroscience
Imbalanced datasets greatly affect the analysis capability of intrusion detection models, biasing their classification results toward normal behavior and leading to high false-positive and false-negative rates. To alleviate the impact of class imbala...

A dynamical neural network approach for solving stochastic two-player zero-sum games.

Neural networks : the official journal of the International Neural Network Society
This paper aims at solving a stochastic two-player zero-sum Nash game problem studied in Singh and Lisser (2019). The main contribution of our paper is that we model this game problem as a dynamical neural network (DNN for short). In this paper, we s...