Area of research
Radiology, Nuclear Medicine and Imaging · Otorhinolaryngology
Research interest
Research focused on Artificial intelligence and Nasopharyngeal carcinoma, with related work in Radiomics, Overfitting, Interpretability. Notable publications include 'Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling', 'Soft Sensor Modeling Method Based on Semisupervised Deep Learning and Its Application to Wastewater Treatment Plant', and 'Building reliable radiomic models using image perturbation'.
Multi-omics and Multi-VOIs to predict esophageal fistula in esophageal cancer patients treated with radiotherapy
Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling
Radiomic feature repeatability and its impact on prognostic model generalizability: A multi-institutional study on nasopharyngeal carcinoma patients
Multimodal Data Integration to Predict Severe Acute Oral Mucositis of Nasopharyngeal Carcinoma Patients Following Radiation Therapy
Building reliable radiomic models using image perturbation
Radiomics-Based Detection of COVID-19 from Chest X-ray Using Interpretable Soft Label-Driven TSK Fuzzy Classifier
Quantitative Spatial Characterization of Lymph Node Tumor for N Stage Improvement of Nasopharyngeal Carcinoma Patients
Integration of an imbalance framework with novel high-generalizable classifiers for radiomics-based distant metastases prediction of advanced nasopharyngeal carcinoma
Synergistic Effects of Ca2+ and High-Valence Nb5+ co-Doping on the Structural, Optical and Magnetic Properties of BiFeO3
Soft Sensor Modeling Method Based on Semisupervised Deep Learning and Its Application to Wastewater Treatment Plant