55 citations · 80 across the 5 of their papers we have counts for
5 papers
RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
Yu Gong, Greg Mori, Frederick Tung
Data imbalance, in which a plurality of the data samples come from a small proportion of labels, poses a challenge in training deep neural networks. Unlike classification, in regre…
Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network
Kehan Qi, Haoran Li, Chuyu Rong +4
Image Quality Assessment (IQA) is of great value in the workflow of Magnetic Resonance Imaging (MRI)-based analysis. Blind IQA (BIQA) methods are especially required since high-qua…
Variational Selective Autoencoder: Learning from Partially-Observed Heterogeneous Data
Yu Gong, Hossein Hajimirsadeghi, Jiawei He +2
Learning from heterogeneous data poses challenges such as combining data from various sources and of different types. Meanwhile, heterogeneous data are often associated with missin…
Multi-task MR Imaging with Iterative Teacher Forcing and Re-weighted Deep Learning
Kehan Qi, Yu Gong, Xinfeng Liu +3
Noises, artifacts, and loss of information caused by the magnetic resonance (MR) reconstruction may compromise the final performance of the downstream applications. In this paper,…
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising
Yu Gong, Hongming Shan, Yueyang Teng +5
Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However…