113 citations · 171 across the 7 of their papers we have counts for
7 papers
Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis
Mojtaba Bahrami, Mahsa Ghorbani, Nassir Navab
The need for a large amount of labeled data in the supervised setting has led recent studies to utilize self-supervised learning to pre-train deep neural networks using unlabeled d…
GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
Mahsa Ghorbani, Mojtaba Bahrami, Anees Kazi +3
The increased amount of multi-modal medical data has opened the opportunities to simultaneously process various modalities such as imaging and non-imaging data to gain a comprehens…
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data
Mahsa Ghorbani, Anees Kazi, Mahdieh Soleymani Baghshah +2
Disease prediction is a well-known classification problem in medical applications. GCNs provide a powerful tool for analyzing the patients' features relative to each other. This ca…
Adversarial Classifier for Imbalanced Problems
Ehsan Montahaei, Mahsa Ghorbani, Mahdieh Soleymani Baghshah +1
Adversarial approach has been widely used for data generation in the last few years. However, this approach has not been extensively utilized for classifier training. In this paper…
MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks
Mahsa Ghorbani, Mahdieh Soleymani Baghshah, Hamid R. Rabiee
Graph embedding is an important approach for graph analysis tasks such as node classification and link prediction. The goal of graph embedding is to find a low dimensional represen…
Stock Price Prediction using Principle Components
Mahsa Ghorbani, Edwin K. P. Chong
The literature provides strong evidence that stock prices can be predicted from past price data. Principal component analysis (PCA) is a widely used mathematical technique for dime…