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Found 5 papers, 4 papers with code
Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis
We show that training the agent against the prediction model can significantly improve the semantic features extracted for downstream classification tasks.
GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
The soft pseudo-labels are then used to train a deep student network for disease prediction of unseen test data for which the graph modality is unavailable.
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data
This is accomplished by associating a graph-based neural network to each class, which is responsible for weighting the class samples and changing the importance of each sample for the classifier.
Adversarial Classifier for Imbalanced Problems
However, this approach has not been extensively utilized for classifier training.
MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks
The goal of graph embedding is to find a low dimensional representation of graph nodes that preserves the graph information.
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