AIMC Topic: Neural Networks, Computer

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Multigraph Transformer for Free-Hand Sketch Recognition.

IEEE transactions on neural networks and learning systems
Learning meaningful representations of free-hand sketches remains a challenging task given the signal sparsity and the high-level abstraction of sketches. Existing techniques have focused on exploiting either the static nature of sketches with convol...

Two-Stage Bayesian Optimization for Scalable Inference in State-Space Models.

IEEE transactions on neural networks and learning systems
State-space models (SSMs) are a rich class of dynamical models with a wide range of applications in economics, healthcare, computational biology, robotics, and more. Proper analysis, control, learning, and decision-making in dynamical systems modeled...

Detecting Anomaly Event in Video Based on Generative Adversarial Network.

Computational intelligence and neuroscience
Anomaly detection in videos is a challenging computer vision problem. Existing state-of-the-art video anomaly detection methods mainly focus on the structural design of deep neural networks to obtain performance improvements. Different from the main ...

Objective assessment of segmentation models for thyroid ultrasound images.

Journal of ultrasound
Ultrasound features related to thyroid lesions structure, shape, volume, and margins are considered to determine cancer risk. Automatic segmentation of the thyroid lesion would allow the sonographic features to be estimated. On the basis of clinical ...

Identification of blood species based on surface-enhanced Raman scattering spectroscopy and convolutional neural network.

Journal of biophotonics
The identification of blood species is of great significance in many aspects such as forensic science, wildlife protection, and customs security and quarantine. Conventional Raman spectroscopy combined with chemometrics is an established method for i...

DGANet: A Dual Global Attention Neural Network for Breast Lesion Detection in Ultrasound Images.

Ultrasound in medicine & biology
Deep learning-based breast lesion detection in ultrasound images has demonstrated great potential to provide objective suggestions for radiologists and improve their accuracy in diagnosing breast diseases. However, the lack of an effective feature en...

Multimodal fusion diagnosis of depression and anxiety based on CNN-LSTM model.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND: In recent years, more and more people suffer from depression and anxiety. These symptoms are hard to be spotted and can be very dangerous. Currently, the Self-Reported Anxiety Scale (SAS) and Self-Reported Depression Scale (SDS) are commo...

Neural network-based event-triggered data-driven control of disturbed nonlinear systems with quantized input.

Neural networks : the official journal of the International Neural Network Society
This paper is devoted to design an event-triggered data-driven control for a class of disturbed nonlinear systems with quantized input. A uniform quantizer reconstructed with decreasing quantization intervals is employed to reduce the quantization er...

A general deep learning framework for neuron instance segmentation based on Efficient UNet and morphological post-processing.

Computers in biology and medicine
Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental step for many biological studies. However, the excellent performance of the neural networks requires ...

Gradient Matters: Designing Binarized Neural Networks via Enhanced Information-Flow.

IEEE transactions on pattern analysis and machine intelligence
Binarized neural networks (BNNs) have drawn significant attention in recent years, owing to great potential in reducing computation and storage consumption. While it is attractive, traditional BNNs usually suffer from slow convergence speed and drama...