Publications by authors named "Ronghua Ling"
Article Synopsis
- The study proposes a new radiomics-guided deep learning model to improve the differential diagnosis of atypical Parkinsonian syndromes, which can be difficult to distinguish.
- Researchers analyzed data from 1495 subjects, including healthy controls and patients with various Parkinsonian disorders, using F-FDG PET scans to develop and validate the model.
- The results showed that this deep learning approach outperformed traditional methods, achieving high sensitivity rates for diagnosing different types of Parkinson's disease and providing interpretable features linked to biological aspects of the disorders.
Early Diagnosis and Biomarkers of Alzheimer's Disease Based on Spatio-temporal Graph Convolution Network.
Annu Int Conf IEEE Eng Med Biol Soc
July 2023
Functional magnetic resonance imaging (fMRI) could detect the dynamic activity of brain function and communication. Previous studies have found reduced brain functional connectivity in Alzheimer's disease (AD) patients. In this study, we proposed to process fMRI data by spatio-temporal graph convolution network (ST-GCN) to achieve an early differential diagnosis of AD and to extract image markers using gradient-weighted class activation mapping (Grad-CAM).
View Article and Find Full Text PDFBidirectional Mapping Perception-enhanced Cycle-consistent Generative Adversarial Network for Super-resolution of Brain MRI images.
Annu Int Conf IEEE Eng Med Biol Soc
July 2023
As an effective tool for visualizing neurodegeneration, high-resolution structural magnetism facilitates quantitative image analysis and clinical applications. Super-resolution reconstruction technology allows to improve the resolution of images without upgrading the scanning hardware. However, existing super-resolution techniques relied on paired image data sets and lacked further quantitative analysis of the generated images.
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