Area of research
Radiology, Nuclear Medicine and Imaging · Computer Vision and Pattern Recognition
Research interest
Research interests include Medical Imaging Techniques and Applications, Radiomics and Machine Learning in Medical Imaging, Advanced Neural Network Applications, and Image and Signal Denoising Methods.
3D Point-Based Multi-Modal Context Clusters GAN for Low-Dose PET Image Denoising
3D multi-modality Transformer-GAN for high-quality PET reconstruction
Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction
Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning
ASMFS: Adaptive-similarity-based multi-modality feature selection for classification of Alzheimer's disease
3D CVT-GAN: A 3D Convolutional Vision Transformer-GAN for PET Reconstruction
Adaptive rectification based adversarial network with spectrum constraint for high-quality PET image synthesis
Tripled-Uncertainty Guided Mean Teacher Model for Semi-supervised Medical Image Segmentation
3D Transformer-GAN for High-Quality PET Reconstruction
DA-DSUnet: Dual Attention-based Dense SU-net for automatic head-and-neck tumor segmentation in MRI images
LR-cGAN: Latent representation based conditional generative adversarial network for multi-modality MRI synthesis
Medical Imaging Based Diagnosis Through Machine Learning and Data Analysis
Medical Image Synthesis via Deep Learning
3D conditional generative adversarial networks for high-quality PET image estimation at low dose
3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis
Locality Adaptive Multi-modality GANs for High-Quality PET Image Synthesis
Multi-modality feature selection with adaptive similarity learning for classification of Alzheimer's disease
Tumor segmentation via multi-modality joint dictionary learning