4 accepted papers
High-resolution (HR) magnetic resonance (MR) images could provide reliable visual information for clinical diagnosis. Recently, super-resolution (SR) methods based on convolutional neural networks (CNNs) have shown great potential in obtaining HR MR images. However, most existing CNN-based SR method…
Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification). However, most existing approaches eithe…
High resolution (HR) magnetic resonance (MR) images are crucial for medical diagnosis. However, in practice, low resolution MR images are often acquired due to hardware limitation. In this work, we propose a gating feature dense network to reconstruct HR MR images from low resolution acquisitions, w…
We propose a novel model-based network to reconstruct the magnetic resonance (MR) image. In this network, the Approximate Message Passing (AMP) algorithm is unrolled to solve the optimization problem of compressed sensing MR imaging, and several CNN blocks is embedded as de-aliasing steps. We relax…