2 papers
eess.IV2023
Self-supervised Noise2noise Method Utilizing Corrupted Images with a Modular Network for LDCT Denoising
Yuting Zhu, Qiang He, Yudong Yao +1
Deep learning is a very promising technique for low-dose computed tomography (LDCT) image denoising. However, traditional deep learning methods require paired noisy and clean datas…
cs.LG2022
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…