Name
Title | ||
|---|---|---|
RA-GCN: Graph convolutional network for disease prediction problems with imbalanced data |
Abstract | ||
|---|---|---|
•Study the class imbalance issue in the context of disease pre- diction problem modeled as a node classification task.•Propose a model to learn weight for each training sample and use it in the loss function to update the classifier.•Utilize an adversarial approach for training the classifier and weighing networks simultaneously.•Evaluation on 3 publicly available and a set of generated synthetic datasets with quantitative and qualitative experiments.•Enhance the performance on real and synthetic datasets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.media.2021.102272 | Medical Image Analysis |
Keywords | DocType | Volume |
Disease prediction,Graphs,Graph convolutional networks,Node classification,Imbalanced classification | Journal | 75 |
ISSN | Citations | PageRank |
1361-8415 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahsa Ghorbani | 1 | 0 | 0.68 |
| Anees Kazi | 2 | 13 | 5.34 |
| Mahdieh Soleymani Baghshah | 3 | 0 | 0.68 |
| Hamid R. Rabiee | 4 | 336 | 41.77 |
| Nassir Navab | 5 | 6594 | 578.60 |