AIMC Topic: Neural Networks, Computer

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RVCNet: A hybrid deep neural network framework for the diagnosis of lung diseases.

PloS one
Early evaluation and diagnosis can significantly reduce the life-threatening nature of lung diseases. Computer-aided diagnostic systems (CADs) can help radiologists make more precise diagnoses and reduce misinterpretations in lung disease diagnosis. ...

Trainable Spiking-YOLO for low-latency and high-performance object detection.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) are considered an attractive option for edge-side applications due to their sparse, asynchronous and event-driven characteristics. However, the application of SNNs to object detection tasks faces challenges in achieving...

Local structure-aware graph contrastive representation learning.

Neural networks : the official journal of the International Neural Network Society
Traditional Graph Neural Network (GNN), as a graph representation learning method, is constrained by label information. However, Graph Contrastive Learning (GCL) methods, which tackles the label problem effectively, mainly focus on the feature inform...

Fixed-time synchronization of complex-valued neural networks for image protection and 3D point cloud information protection.

Neural networks : the official journal of the International Neural Network Society
This paper studies the fixed-time synchronization (FDTS) of complex-valued neural networks (CVNNs) based on quantized intermittent control (QIC) and applies it to image protection and 3D point cloud information protection. A new controller was design...

Physics-guided neural network for predicting asphalt mixture rutting with balanced accuracy, stability and rationality.

Neural networks : the official journal of the International Neural Network Society
The prediction of rutting performance of asphalt materials poses a significant challenge due to the intricate relationships between the rutting performance and its influencing factors. Machine learning models have gained popularity to address this ch...

EEGProgress: A fast and lightweight progressive convolution architecture for EEG classification.

Computers in biology and medicine
Because of the intricate topological structure and connection of the human brain, extracting deep spatial features from electroencephalograph (EEG) signals is a challenging and time-consuming task. The extraction of topological spatial information pl...

SMILE: Siamese Multi-scale Interactive-representation LEarning for Hierarchical Diffeomorphic Deformable image registration.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Deformable medical image registration plays an important role in many clinical applications. It aims to find a dense deformation field to establish point-wise correspondences between a pair of fixed and moving images. Recently, unsupervised deep lear...

A scale conjugate neural network approach for the fractional schistosomiasis disease system.

Computer methods in biomechanics and biomedical engineering
This study presents the numerical solutions of the fractional schistosomiasis disease model (SDM) using the supervised neural networks (SNNs) and the computational scaled conjugate gradient (SCG), i.e. SNNs-SCG. The fractional derivatives are used fo...

Crowdsourcing image segmentation for deep learning: integrated platform for citizen science, paid microtask, and gamification.

Biomedizinische Technik. Biomedical engineering
OBJECTIVES: Segmentation is crucial in medical imaging. Deep learning based on convolutional neural networks showed promising results. However, the absence of large-scale datasets and a high degree of inter- and intra-observer variations pose a bottl...

Adversarially robust neural networks with feature uncertainty learning and label embedding.

Neural networks : the official journal of the International Neural Network Society
Deep neural networks (DNNs) are vulnerable to the attacks of adversarial examples, which bring serious security risks to the learning systems. In this paper, we propose a new defense method to improve the adversarial robustness of DNNs based on stoch...