2 citations · 5 across the 5 of their papers we have counts for
5 papers
PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers
Wenhui Zhu, Peijie Qiu, Xiwen Chen +2
Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags. However, due to thei…
SFCNeXt: a simple fully convolutional network for effective brain age estimation with small sample size
Yu Fu, Yanyan Huang, Shunjie Dong +4
Deep neural networks (DNN) have been designed to predict the chronological age of a healthy brain from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age…
Optimal Transport Guided Unsupervised Learning for Enhancing low-quality Retinal Images
Wenhui Zhu, Peijie Qiu, Mohammad Farazi +3
Real-world non-mydriatic retinal fundus photography is prone to artifacts, imperfections and low-quality when certain ocular or systemic co-morbidities exist. Artifacts may result…
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing
Wenhui Zhu, Peijie Qiu, Oana M. Dumitrascu +5
Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operator…
Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentation
Wenhui Zhu, Peijie Qiu, Natasha Lepore +2
Lesion appearance is a crucial clue for medical providers to distinguish referable diabetic retinopathy (rDR) from non-referable DR. Most existing large-scale DR datasets contain o…