A5087831401- Advanced Image Processing Techniques
- Recommender Systems and Techniques
- Advanced Graph Neural Networks
- Medical Imaging Techniques and Applications
- Legal and Policy Issues
- Radiomics and Machine Learning in Medical Imaging
- Advanced Research in Systems and Signal Processing
- Myasthenia Gravis and Thymoma
- Caching and Content Delivery
- Seismic Imaging and Inversion Techniques
- Topic Modeling
- Meningioma and schwannoma management
- Image Enhancement Techniques
- Image and Video Quality Assessment
Chinese University of Hong Kong
2024
Ministry of Education of the People's Republic of China
2024
Shanghai Jiao Tong University
2024
Shanghai First People's Hospital
2024
University of Shanghai for Science and Technology
2024
Xinjiang University
2024
Wuhan University
2021
In this letter, a method called Semantic Information Oriented No-Reference (SIONR) video quality assessment model is developed, which can effectively represent degradation of by taking the variations semantic information into consideration. Specially, temporal features between adjacent frames are calculated to consider inconsistency static information. Moreover, low-level also applied as supplementary take distortions related local details Experimental results demonstrate that our proposed...
Abstract Objectives To develop and validate nomograms combining radiomics semantic features to identify the invasiveness histopathological risk stratification of thymic epithelial tumors (TET) using contrast-enhanced CT. Methods This retrospective multi-center study included 224 consecutive cases. For each case, 6764 intratumor peritumor 31 were collected. Multi-feature selections decision tree models performed on separately select most important for Masaoka–Koga staging WHO classification....
Social recommendation systems play a vital role in today's Internet era. The richness of social relationships can compensate for the sparsity interaction data between users and items, thereby reducing impact this on systems. Typically, utilize graph to describe users, items their relationships. So using Graph Neural Networks (GNN) effectively analyze complex nodes. However, traditional GNN models primarily focus node neighbors during information propagation, which may hinder capturing global...
Abstract Computational enhancement is an important strategy for inferring high-resolution features from genome-wide chromosome conformation capture (Hi-C) data, which typically have limited resolution. Deep learning has been highly successful in this task but we show that it creates prevalent artificial structures the enhanced data due to need divide large contact matrix into small patches. In addition, previous deep methods largely focus on local patterns, cannot fully complexity of Hi-C...