220 citations · 296 across the 22 of their papers we have counts for
6 papers · 1 filter
QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data
Yuhui Ruan, Qiao Yuan, Chuang Niu +4
Deep learning has been successfully applied to low-dose CT (LDCT) image denoising for reducing potential radiation risk. However, the widely reported supervised LDCT denoising netw…
Adaptive Weighted Nonnegative Matrix Factorization for Robust Feature Representation
Tingting Shen, Junhang Li, Can Tong +4
Nonnegative matrix factorization (NMF) has been widely used to dimensionality reduction in machine learning. However, the traditional NMF does not properly handle outliers, so that…
Improving the Level of Autism Discrimination through GraphRNN Link Prediction
Haonan Sun, Qiang He, Shouliang Qi +2
Dataset is the key of deep learning in Autism disease research. However, due to the few quantity and heterogeneity of samples in current dataset, for example ABIDE (Autism Brain Im…
An Entropy Weighted Nonnegative Matrix Factorization Algorithm for Feature Representation
Jiao Wei, Can Tong, Bingxue Wu +4
Nonnegative matrix factorization (NMF) has been widely used to learn low-dimensional representations of data. However, NMF pays the same attention to all attributes of a data point…
Quadratic Autoencoder (Q-AE) for Low-dose CT Denoising
Fenglei Fan, Hongming Shan, Mannudeep K. Kalra +6
Inspired by complexity and diversity of biological neurons, our group proposed quadratic neurons by replacing the inner product in current artificial neurons with a quadratic opera…
Soft Autoencoder and Its Wavelet Adaptation Interpretation
Fenglei Fan, Mengzhou Li, Yueyang Teng +1
Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields. However, deep learning is criticized for lack of interpr…