Hu Chen

TL;DR: This work combines the autoencoder, deconvolution network, and shortcut connections into the residual encoder–decoder convolutional neural network (RED-CNN) for low-dose CT imaging and achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases.

TL;DR: A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion, demonstrating a great potential of the proposed method on artifact reduction and structure preservation.

TL;DR: In this paper, a learned experts' assessment-based reconstruction network (LEARN) was proposed for sparse-data computed tomography (CT) reconstruction, which utilizes application-oriented knowledge more effectively and recovers underlying images more favorably than competing algorithms.

TL;DR: Zhang et al. as discussed by the authors combined the autoencoder, the deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging.

TL;DR: This paper introduces an MRI denoising method based on the residual encoder-decoder Wasserstein generative adversarial network (RED-WGAN), which demonstrates powerful abilities in both noise suppression and structure preservation.