An automatic CT pipeline combining a spatially aware neural network, tensor-based graph cuts, and Voronoi diagrams estimates renal vascular dominant regions with 80% Dice on 8 cases.
Spatial PixelCNN: Generating Images from Patches
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In this paper we propose Spatial PixelCNN, a conditional autoregressive model that generates images from small patches. By conditioning on a grid of pixel coordinates and global features extracted from a Variational Autoencoder (VAE), we are able to train on patches of images, and reproduce the full-sized image. We show that it not only allows for generating high quality samples at the same resolution as the underlying dataset, but is also capable of upscaling images to arbitrary resolutions (tested at resolutions up to $50\times$) on the MNIST dataset. Compared to a PixelCNN++ baseline, Spatial PixelCNN quantitatively and qualitatively achieves similar performance on the MNIST dataset.
fields
eess.IV 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Precise Estimation of Renal Vascular Dominant Regions Using Spatially Aware Fully Convolutional Networks, Tensor-Cut and Voronoi Diagrams
An automatic CT pipeline combining a spatially aware neural network, tensor-based graph cuts, and Voronoi diagrams estimates renal vascular dominant regions with 80% Dice on 8 cases.