A5039028263- Radiomics and Machine Learning in Medical Imaging
- Pancreatic and Hepatic Oncology Research
- Sports Performance and Training
- Medical Imaging and Analysis
- Advanced X-ray and CT Imaging
- Photoacoustic and Ultrasonic Imaging
- Sports injuries and prevention
- Renal cell carcinoma treatment
- Human Pose and Action Recognition
- CCD and CMOS Imaging Sensors
- Biometric Identification and Security
- Brain Tumor Detection and Classification
- Spectroscopy Techniques in Biomedical and Chemical Research
Shandong University of Science and Technology
2023-2025
First Affiliated Hospital of Fujian Medical University
2024
Fujian Medical University
2024
Medical Architecture (United Kingdom)
2023
This research aimed to assess the value of radiomics combined with multiple machine learning algorithms in diagnosis pancreatic ductal adenocarcinoma (PDAC) lymph node (LN) metastasis, which is expected provide clinical treatment strategies.
Applying computer vision and machine learning techniques into sport tests is an effective way to realize “intelligent sports.” Facing practical application, we design a real-time lightweight deep network intelligent pull-ups test in this study. The main contributions are as follows: (1) new self-produced dataset established under the requirement of including human body horizontal bar. In addition, semiautomatic annotating software developed enhance annotation efficiency increase labeling...
Objective The aim of this study was to evaluate the prognostic potential combining clinical features and radiomics with multiple machine learning (ML) algorithms in pancreatic ductal adenocarcinoma (PDAC). Methods A total 116 patients PDAC who met eligibility criteria were randomly assigned a training or validation cohort. Seven ML algorithms, including Supervised Principal Components, stepwise Cox, Random Survival Forest, CoxBoost, Least absolute shrinkage selection operation (Lasso),...