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Computed Tomography (CT)

The term “computed tomography”, or CT, refers to a computerized x-ray imaging procedure in which a narrow beam of x-rays is aimed at a patient and quickly rotated around the body, producing signals that are processed by the machine's computer to generate cross-sectional images—or “slices”—of the body.

( Image credit: Liver Lesion Detection from Weakly-labeled Multi-phase CT Volumes with a Grouped Single Shot MultiBox Detector )

Papers

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Showing 976–1000 of 1207 papers

TitleStatusHype
Improved Material Decomposition with a Two-step Regularization for spectral CT—0
Extreme Few-view CT Reconstruction using Deep Inference—0
A Deep Learning System That Generates Quantitative CT Reports for Diagnosing Pulmonary Tuberculosis—0
1-point RANSAC for Circular Motion Estimation in Computed Tomography (CT)—0
Function Follows Form: Regression from Complete Thoracic Computed Tomography Scans—0
Deep-learning-based Breast CT for Radiation Dose Reduction—0
Optimal Transport driven CycleGAN for Unsupervised Learning in Inverse Problems—0
Invasiveness Prediction of Pulmonary Adenocarcinomas Using Deep Feature Fusion Networks—0
Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories—0
Z-Net: an Anisotropic 3D DCNN for Medical CT Volume Segmentation—0
Efficient 3D Fully Convolutional Networks for Pulmonary Lobe Segmentation in CT ImagesCode0
Tapering Analysis of Airways with BronchiectasisCode1
Local block-wise self attention for normal organ segmentation—0
MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion DetectionCode0
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms—0
Integrating Data and Image Domain Deep Learning for Limited Angle Tomography using Consensus Equilibrium—0
3D Anchor-Free Lesion Detector on Computed Tomography Scans—0
Are Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters?—0
Shape-Aware Complementary-Task Learning for Multi-Organ SegmentationCode0
MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and SegmentationCode0
Kidney and Kidney Tumor Segmentation using a Logical Ensemble of U-nets with Volumetric Validation—0
SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis—0
Multi-Contrast Super-Resolution MRI Through a Progressive Network—0
BCD-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization—0
Automatic segmentation of kidney and liver tumors in CT images—0
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