Neural networks : the official journal of the International Neural Network Society
Jul 22, 2024
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. Most existing DCMVC algorithms focus on exploring the consistency infor...
Neural networks : the official journal of the International Neural Network Society
Jul 22, 2024
Recent successes in robot learning have significantly enhanced autonomous systems across a wide range of tasks. However, they are prone to generate similar or the same solutions, limiting the controllability of the robot to behave according to user i...
Neural networks : the official journal of the International Neural Network Society
Jul 22, 2024
This study is centered around the dynamic behaviors observed in a class of fractional-order generalized reaction-diffusion inertial neural networks (FGRDINNs) with time delays. These networks are characterized by differential equations involving two ...
Neural networks : the official journal of the International Neural Network Society
Jul 22, 2024
Person re-identification (ReID) has made good progress in stationary domains. The ReID model must be retrained to adapt to new scenarios (domains) as they emerge unexpectedly, which leads to catastrophic forgetting. Continual learning trains the mode...
Neural networks : the official journal of the International Neural Network Society
Jul 22, 2024
This paper addresses the asynchronous control problem for semi-Markov reaction-diffusion neural networks (SMRDNNs) under probabilistic event-triggered protocol (PETP) scheduling. A semi-Markov process with a deterministic switching rule is introduced...
Neural networks : the official journal of the International Neural Network Society
Jul 22, 2024
Lossy image coding techniques usually result in various undesirable compression artifacts. Recently, deep convolutional neural networks have seen encouraging advances in compression artifact reduction. However, most of them focus on the restoration o...
PURPOSE: The purpose of this study is to develop and apply an algorithm that automatically classifies spine radiographs of pediatric scoliosis patients.
Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-Net face limitations in modeling long-range dependencies. To address this, Transformers designed for sequence-to-sequence predictions have been integrated into...
Computer methods and programs in biomedicine
Jul 22, 2024
BACKGROUND: Breast cancer remains a leading cause of female mortality worldwide, exacerbated by limited awareness, inadequate screening resources, and treatment options. Accurate and early diagnosis is crucial for improving survival rates and effecti...
Model quantization is a promising technique that can simultaneously compress and accelerate a deep neural network by limiting its computation bit-width, which plays a crucial role in the fast-growing AI industry. Despite model quantization's success ...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.