A5082547911- Functional Brain Connectivity Studies
- Advanced Neuroimaging Techniques and Applications
- Brain Tumor Detection and Classification
- Dementia and Cognitive Impairment Research
- Advanced Image Processing Techniques
- Medical Image Segmentation Techniques
- Machine Learning in Healthcare
- Gene Regulatory Network Analysis
- Energy Harvesting in Wireless Networks
- EEG and Brain-Computer Interfaces
- Image Enhancement Techniques
- Image and Signal Denoising Methods
- Generative Adversarial Networks and Image Synthesis
- AI in cancer detection
- Advanced MIMO Systems Optimization
- Neural dynamics and brain function
- Advanced MRI Techniques and Applications
- Medical Imaging and Analysis
- Advanced Neural Network Applications
- 3D Shape Modeling and Analysis
- Cell Image Analysis Techniques
- Advancements in Solid Oxide Fuel Cells
- UAV Applications and Optimization
- Fractional Differential Equations Solutions
- Fault Detection and Control Systems
Shenzhen Institutes of Advanced Technology
2016-2025
Institute of Applied Ecology
2023-2025
Chinese Academy of Sciences
2014-2025
First Affiliated Hospital of Dalian Medical University
2025
Hubei University of Technology
2019-2024
Fuzhou University
2020-2024
Hebei University of Engineering
2014-2024
Yueyang Hospital
2022-2024
Shanghai University of Traditional Chinese Medicine
2011-2024
University of Electronic Science and Technology of China
2020-2024
Magnetic resonance (MR) imaging plays an important role in clinical and brain exploration. However, limited by factors such as hardware, scanning time, cost, it is challenging to acquire high-resolution MR images clinically. In this article, fine perceptive generative adversarial networks (FP-GANs) are proposed produce super-resolution (SR) from the low-resolution counterparts. By adopting divide-and-conquer scheme, FP-GANs designed deal with low-frequency (LF) high-frequency (HF) components...
It is of great significance to apply deep learning for the early diagnosis Alzheimer's disease (AD). In this work, a novel tensorizing GAN with high-order pooling proposed assess mild cognitive impairment (MCI) and AD. By three-player cooperative game-based framework, model can benefit from structural information brain. incorporating scheme into classifier, make full use second-order statistics holistic magnetic resonance imaging (MRI). To best our knowledge, Tensor-train, High-order...
The diagnosis of early stages Alzheimer's disease (AD) is essential for timely treatment to slow further deterioration. Visualizing the morphological features AD great clinical value. In this work, a novel multidirectional perception generative adversarial network (MP-GAN) proposed visualize indicating severity patients different stages. Specifically, by introducing mapping mechanism into model, MP-GAN can capture salient global efficiently. Thus, using class discriminative map from...
Alzheimer's disease (AD) is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. Existing auxiliary diagnostic methods have accomplished classification task, but few them can accurately evaluate changing characteristics brain connectivity. In this work, a prior-guided adversarial learning with hypergraph (PALH) model proposed to predict abnormal connections using triple-modality medical images. Concretely, prior...
Brain network analysis plays an increasingly important role in studying brain function and the exploring of disease mechanisms. However, existing construction tools have some limitations, including dependency on empirical users, weak consistency repeated experiments time-consuming processes. In this work, a diffusion-based pipeline, DGCL is designed for end-to-end networks. Initially, region-aware module (BRAM) precisely determines spatial locations regions by diffusion process, avoiding...
Diabetic retinopathy (DR) is one of the major causes blindness. It great significance to apply deep-learning techniques for DR recognition. However, algorithms often depend on large amounts labeled data, which expensive and time-consuming obtain in medical imaging area. In addition, features are inconspicuous spread out over high-resolution fundus images. Therefore, it a big challenge learn distribution such features. This article proposes multichannel-based generative adversarial network...
Abstract The 2019 coronavirus disease (COVID-19) outbreak caused by the SARS-CoV-2 virus is an ongoing global health emergency. However, virus’ pathogenesis remains unclear, and there no cure for disease. We investigated dynamic changes of blood immune response in patients with COVID-19 at different stages using 5’ gene expression, T cell receptor (TCR), B receptors (BCR) V(D)J transcriptome analysis a single-cell resolution. obtained mRNA sequencing (scRNA-seq) data 341,420 peripheral...
Renal inflammation is a critical pathophysiological characteristic of diabetic kidney disease (DKD). The mechanism the inflammatory response complicated, and there are few effective treatments for renal that can be used clinically. Insulin-like growth factor-binding protein 5 (IGFBP5) an important secretory related to fibrosis in several tissues. Studies have shown IGFBP5 level significantly upregulated DKD. However, function its DKD remain unclear. Here, we showed levels were increased...
This study investigates Caputo-Hadamard fractional differential equations on time scales. The Hadamard sum and difference are defined for the first time. A general logarithm function scales is used as a kernel function. New their equivalent presented by use of fundamental theorems. Gronwall inequality, asymptotical stability conditions, two discrete-time Mittag-Leffler functions type obtained. Numerical schemes provided chaos in logistic equation neural network reported.
Integrating the brain structural and functional connectivity features is of great significance in both exploring science analyzing cognitive impairment clinically. However, it remains a challenge to effectively fuse complex network. In this paper, novel structure-function fusing-representation learning (BSFL) model proposed learn fused representation from diffusion tensor imaging (DTI) resting-state magnetic resonance (fMRI) for mild (MCI) analysis. Specifically, decomposition-fusion...
Vignetting commonly occurs as a degradation in images resulting from factors such lens design, improper hood usage, and limitations camera sensors. This affects image details, color accuracy, presents challenges computational photography. Existing vignetting removal algorithms predominantly rely on ideal physics assumptions hand-crafted parameters, the ineffective of irregular suboptimal results. Moreover, substantial lack real-world datasets hinders objective comprehensive evaluation...
Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders effective fusion features. Moreover, achieving reliable and interpretable diagnoses in field remains challenging. To address them, we propose a novel network based on multi-fusion disease-induced learning (MDL-Net) to enhance AD by efficiently fusing data. Specifically, MDL-Net proposes joint...