Q
Qi Zhang
Researcher at Fudan University
Publications - 131
Citations - 2439
Qi Zhang is an academic researcher from Fudan University. The author has contributed to research in topics: Medicine & Ultrasound. The author has an hindex of 20, co-authored 100 publications receiving 1563 citations. Previous affiliations of Qi Zhang include Minjiang University & Duke University.
Papers
More filters
Journal ArticleDOI
Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease
TL;DR: Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.
Journal ArticleDOI
Deep learning based classification of breast tumors with shear-wave elastography.
TL;DR: A deep learning architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE) that integrates feature learning with feature selection on SWE is built and may be potentially used in clinical computer-aided diagnosis of breast cancer.
Journal ArticleDOI
CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients.
Fengjun Liu,Qi Zhang,Chao Huang,Chunzi Shi,Lin Wang,Nannan Shi,Cong Fang,Fei Shan,Xue Mei,Jing Shi,Fengxiang Song,Zhongcheng Yang,Zezhen Ding,Xiaoming Su,Hongzhou Lu,Tongyu Zhu,Zhiyong Zhang,Lei Shi,Yuxin Shi +18 more
TL;DR: CT quantification of pneumonia lesions can early and non-invasively predict the progression to severe illness, providing a promising prognostic indicator for clinical management of COVID-19.
Journal ArticleDOI
Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset
TL;DR: A stacked DPN (S-DPN) algorithm is proposed to further improve the representation performance of the original DPN, and S-DPn is applied to the task of texture feature learning for ultrasound based tumor classification with small dataset, suggesting that S- DPN can be a strong candidate for the texture feature representation learning on small ultrasound datasets.
Journal ArticleDOI
Super-resolution reconstruction of MR image with a novel residual learning network algorithm.
TL;DR: This work proposes a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL), which works effectively in capturing high-frequency details by learning local residuals.