Qi Zhang

TL;DR: Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.

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.

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.

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.

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.