Digital chest tomosynthesis (DCT) is a technique to produce sectional 3D images of a human chest for pulmonary disease screening, with 2D X-ray projections taken within an extremely limited range of angles.
Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems.
This class of automata captures nominal regular languages; analogously to the classical language theory, nominal automata have been shown to characterise nominal regular expressions with binders.
On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals.
The latest deep learning-based approaches have shown promising results for the challenging task of inpainting missing regions of an image.
Ranked #1 on Image Inpainting on Paris StreetView
We investigate the effectiveness of a simple solution to the common problem of deep learning in medical image analysis with limited quantities of labeled training data.
To solve this problem, we present a novel deep colorization method, which allows simultaneous global and local inputs to better control the output colorized images.
The convolutional neural pyramids extract information from large receptive fields of the depth map and guidance map, while the convolutional neural network effectively transfers useful structures of the guidance image to the depth image.