25 citations · 61 across the 11 of their papers we have counts for
4 papers · 1 filter
Toward Improved Generalization: Meta Transfer of Self-supervised Knowledge on Graphs
Wenhui Cui, Haleh Akrami, Anand A. Joshi +1
Despite the remarkable success achieved by graph convolutional networks for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging…
Learning From Positive and Unlabeled Data Using Observer-GAN
Omar Zamzam, Haleh Akrami, Richard Leahy
The problem of learning from positive and unlabeled data (A.K.A. PU learning) has been studied in a binary (i.e., positive versus negative) classification setting, where the input…
Learning from imperfect training data using a robust loss function: application to brain image segmentation
Haleh Akrami, Wenhui Cui, Anand A Joshi +1
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis…
Semi-supervised Learning using Robust Loss
Wenhui Cui, Haleh Akrami, Anand A. Joshi +1
The amount of manually labeled data is limited in medical applications, so semi-supervised learning and automatic labeling strategies can be an asset for training deep neural netwo…