A5102788624- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Brain Tumor Detection and Classification
- Medical Imaging and Analysis
- Infrared Target Detection Methodologies
- Machine Learning in Healthcare
- Advanced Image Fusion Techniques
- Advanced Measurement and Detection Methods
- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Dental materials and restorations
- Dental Implant Techniques and Outcomes
- Dental Research and COVID-19
- Color Science and Applications
Guangzhou Medical University
2024
University of California, San Francisco
2023
University of San Francisco
2023
University of California, Berkeley
2023
Shandong First Medical University
2020
Tianjin University
2008-2020
With the popularization of digital technology and exposure traditional technology’s defects, computer-aided design manufacturing (CAD/CAM) has been widely used in field dentistry. And accuracy scanning system determines ultimate prosthesis, which is a very important part CAD/CAM, so we decided to evaluate intraoral extraoral scanners. In this study, selected sphere model as object obtained final result through data analysis 3D fitting. terms trueness precision, scanner SHINING was...
Summary Autonomous driving has gradually moved towards practical applications in recent years. It is particularly critical to provide reliable real‐time environmental information for autonomous systems. At present, vehicle video surveillance systems based on multi‐source and target detection algorithms can effectively solve these problems. However, the previous are often unable balance effect frame rate. Therefore, we will introduce a system parallel computing computer vision this article....
Vessel density within tumor tissues strongly correlates with proliferation and serves as a critical marker for grading. Recognition of vessel by pathologists is subject to strong inter-rater bias, thus limiting its prognostic value. There are many challenges in the task object detection pathological images, including complex image backgrounds, dense distribution small targets, insignificant differences between features target be detected background. To address these problems help physicians...
Abstract Deep learning transformer models have exhibited exceptional performance in various clinical tasks, including cancer outcome prediction, when applied to electronic health records (EHR). Inspired by the success of bidirectional encoder representations from transformers (BERT) natural language processing, we present OncoBERT, a deep transfer framework tailored for prediction using unstructured notes diverse sites Glioma, Prostate, and Breast. OncoBERT adapts BERT EHR processing employs...
This paper describes a method to characterizing the digital camera. The nonlinear relationship between RGB signals generated by camera and original image CIEXYZ values was obtained using polynomial regression procedures. reasonable structures of were found for two cameras. better number terms 19, yielding modeling accuracy typically averaging 2.1~2.2 E ∆ units maximally 9.5~10.9 units. experiments results showed that could be used characterize commonly