Area of research
Radiology, Nuclear Medicine and Imaging · Pulmonary and Respiratory Medicine
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, Lung Cancer Diagnosis and Treatment, Gastric Cancer Management and Outcomes, and Head and Neck Cancer Studies.
The application of artificial intelligence in upper gastrointestinal cancers
ContraSurv: Enhancing Prognostic Assessment of Medical Images via Data-Efficient Weakly Supervised Contrastive Learning
Forging trust in AI-assisted disease diagnosis
HiCur-NPC: Hierarchical Feature Fusion Curriculum Learning for Multi-Modal Foundation Model in Nasopharyngeal Carcinoma
Artificial intelligence in medical imaging empowers precision neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma.
Annotation-free artificial intelligence for abdominal computed tomography anomaly detection.
PCRFed: personalized federated learning with contrastive representation for non-independently and identically distributed medical image segmentation.
CholecMamba: A Mamba-Based Multimodal Reasoning Model for Cholecystectomy Surgery
Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review
Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma
Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma.
Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer.
Deep learning model based on primary tumor to predict lymph node status in clinical stage IA lung adenocarcinoma: a multicenter study.
TripleSurv: Triplet Time-Adaptive Coordinate Learning Approach for Survival Analysis
Fluorescence image-guided tumour surgery
Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review
Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma.
A multi-view co-training network for semi-supervised medical image-based prognostic prediction.
Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study
Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study.
Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma
Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma.
Artificial intelligence in gastric cancer: applications and challenges.
The potential of prostate gland radiomic features in identifying the Gleason score
Development and Validation of a Deep Learning Model to Screen for Trisomy 21 During the First Trimester From Nuchal Ultrasonographic Images.
Deep learning for predicting immunotherapeutic efficacy in advanced non-small cell lung cancer patients: a retrospective study combining progression-free survival risk and overall survival risk.
Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer.
Development of a deep learning‐based nomogram for predicting lymph node metastasis in cervical cancer: A multicenter study
Development of a deep learning-based nomogram for predicting lymph node metastasis in cervical cancer: A multicenter study.
Automatic captioning of early gastric cancer using magnification endoscopy with narrow-band imaging.