Area of research
Radiology, Nuclear Medicine and Imaging · Computer Vision and Pattern Recognition
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, Ultrasound Imaging and Elastography, Medical Image Segmentation Techniques, and AI in cancer detection.
Multimodal Fusion of 3D CT and Pathological Images for Gastric Cancer Recurrence Prediction.
Clinic-aligned Dual Distillation of Video and Image Foundation Models for Automated Breast Cancer US Diagnosis.
An AI-assisted fluorescence microscopic system for screening mitophagy inducers by simultaneous analysis of mitophagic intermediates
An AI-assisted fluorescence microscopic system for screening mitophagy inducers by simultaneous analysis of mitophagic intermediates.
Research on CT image segmentation and classification of liver tumors based on attention mechanism and improved U-Net model
Evaluation of radiosensitivity for high grade gliomas patients using a multi-temporal graph convolutional networks.
Efficient 4D fMRI analysis via spatio-temporal screening and region-aware feature extraction for template-free brain disorder classification.
Label-free navigation system for grading prostate tumour malignancy in situ via tissue pH and prostate-specific antigen activity.
MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma
Cross-View Image Geo-Localization with Panorama-BEV Co-retrieval Network
Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model.
Preoperative Ultrasound Radomics to Predict Posthepatectomy Liver Failure in Patients With Hepatocellular Carcinoma.
Deep learning radiopathomics based on preoperative US images and biopsy whole slide images can distinguish between luminal and non-luminal tumors in early-stage breast cancers
Joint semantic–geometric learning for polygonal building segmentation from high-resolution remote sensing images
Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis
Virtual elastography ultrasound via generative adversarial network for breast cancer diagnosis.
Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning
OCIF: automatically learning the optimized clinical information fusion method for computer-aided diagnosis tasks.
Intelligent SERS Navigation System Guiding Brain Tumor Surgery by Intraoperatively Delineating the Metabolic Acidosis
An efficient R-Transformer network with dual encoders for brain glioma segmentation in MR images
MRI-based brain tumor segmentation using FPGA-accelerated neural network
A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas.
Author Correction: Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.
Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer
Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.
Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics
Lymph node metastasis prediction of papillary thyroid carcinoma based on transfer learning radiomics.
Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis
An Ultrasound Radiomics Nomogram for Preoperative Prediction of Central Neck Lymph Node Metastasis in Papillary Thyroid Carcinoma
Automatic detection of intracranial aneurysms in 3D-DSA based on a Bayesian optimized filter.