Search Results for author:
Found 5 papers, 1 papers with code
Improving image quality of sparse-view lung tumor CT images with U-Net
Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views.
Improving Automated Hemorrhage Detection in Sparse-view Computed Tomography via Deep Convolutional Neural Network based Artifact Reduction
Results: The U-Net performed superior compared to unprocessed and TV-processed images with respect to image quality and automated hemorrhage diagnosis.
Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging
Comparably, with input images manually adjusted to the emphysema window, the DenseNet model predicted COPD with a mean AUC of 0. 86 [0. 82, 0. 89].
WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layer
We investigate the performance of the network on sparse-view chest CT scans, and we highlight the added benefit of having a trainable reconstruction layer over the more conventional fixed ones.
U-Net-based Lung Thickness Map for Pixel-level Lung Volume Estimation of Chest X-rays
Purpose: We aimed to estimate the total lung volume (TLV) from real and synthetic frontal X-ray radiographs on a pixel level using lung thickness maps generated by a U-Net.
