Area of research
Radiology, Nuclear Medicine and Imaging · Pulmonary and Respiratory Medicine
Research interest
Research interests include Radiomics and Machine Learning in Medical Imaging, MRI in cancer diagnosis, Lung Cancer Diagnosis and Treatment, and AI in cancer detection.
General lightweight framework for vision foundation model supporting multi-task and multi-center medical image analysis
Multimodal radiopathological integration for prognosis and prediction of adjuvant chemotherapy benefit in resectable lung adenocarcinoma: A multicentre study
A meta-learning-based robust federated learning for diagnosing lung adenocarcinoma and tuberculosis granulomas
A generalized heterogeneous federated model for identifying patients with postoperative progression of early-stage non-small cell lung cancer
Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence
Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer
A Transfer Learning Radiomics Nomogram for Preoperative Prediction of Borrmann Type IV Gastric Cancer From Primary Gastric Lymphoma
Downregulation of the circadian rhythm regulator HLF promotes multiple-organ distant metastases in non-small cell lung cancer through PPAR/NF-κb signaling
Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule
Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas
A CT-based radiomics nomogram for prediction of lung adenocarcinomas and granulomatous lesions in patient with solitary sub-centimeter solid nodules