Name
Title | ||
|---|---|---|
Dynamic Hyper-Graph Inference Framework for Computer Assisted Diagnosis of Neurodegenerative Diseases. |
Abstract | ||
|---|---|---|
Hyper-graph techniques have been widely investigated in computer vision and medical imaging applications, showing superior performance for modeling complex subject-wise relationships and sufficient flexibility to deal with missing data from multi-modal neuroimaging data. Existing hyper-graph methods, however, are inadequate for two reasons. First, representations are generated only from the observ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMI.2018.2868086 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
Imaging,Training,Testing,Diseases,Data models,Training data,Neuroimaging | Computer vision,Data modeling,Graph,Regression,Medical imaging,Inference,Artificial intelligence,Missing data,Neuroimaging,Cognition,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
38 | 2 | 0278-0062 |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingying Zhu | 1 | 10 | 2.51 |
| Xiaofeng Zhu | 2 | 2 | 0.37 |
| Minjeong Kim | 3 | 435 | 50.98 |
| jin yan | 4 | 6 | 3.18 |
| Daniel Kaufer | 5 | 2 | 0.71 |
| Wu Guorong | 6 | 30 | 4.44 |