Area of research
Psychiatry and Mental health · Cognitive Neuroscience
Research interest
Research focused on Neuroimaging and Biomarker, with related work in Artificial intelligence, Dementia, Cohort. Notable publications include 'Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer’s...', 'Use of radiomic features and support vector machine to distinguish Parkinson’s disease cases from normal controls', and 'Staging tau pathology with tau PET in Alzheimer’s disease: a longitudinal study'.
Impaired glymphatic function as a biomarker for subjective cognitive decline: An exploratory dual cohort study
Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson’s disease: a systematic review and meta-analysis
A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer’s disease
Altered limbic functional connectivity in individuals with subjective cognitive decline: Converging and diverging findings across Chinese and German cohorts
Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition
Asymmetric convolution Swin transformer for medical image super-resolution
Staging tau pathology with tau PET in Alzheimer’s disease: a longitudinal study
Improved detection performance in blood cell count by an attention-guided deep learning method
Artifact removal in photoacoustic tomography with an unsupervised method
An arrhythmia classification algorithm using C-LSTM in physiological parameters monitoring system under internet of health things environment
Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer’s dementia
Use of radiomic features and support vector machine to distinguish Parkinson’s disease cases from normal controls
Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer’s Disease
Radiomics: a novel feature extraction method for brain neuron degeneration disease using <sup>18</sup>F-FDG PET imaging and its implementation for Alzheimer’s disease and mild cognitive impairment
Predicting Alzheimer Disease From Mild Cognitive Impairment With a Deep Belief Network Based on 18F-FDG-PET Images
Area and volume ratios for prediction of visual outcome in idiopathic macular hole
Differences in Aβ brain networks in Alzheimer's disease and healthy controls