Xiaohuan Cao

TL;DR: Wang et al. as mentioned in this paper developed a dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT), and proposed a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses.

TL;DR: A 3D fully convolutional neural network is adopted to learn an end-to-end nonlinear mapping from MR image to CT image and this method is accurate and robust for predicting CT image from MRI image, and also outperforms three state-of-the-art methods under comparison.

TL;DR: Zhang et al. as discussed by the authors designed a fully convolutional network that is subject to dual-guidance: ground-truth guidance using deformation fields obtained by an existing registration method; and image dissimilarity guidance using the difference between the images after registration.

TL;DR: A convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair to their corresponding deformation field, and it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable.

TL;DR: A dual-sampling attention network to automatically diagnose COVID-19 from the community acquired pneumonia (CAP) in chest computed tomography (CT) with a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses.