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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 876–900 of 1207 papers

TitleStatusHype
Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography—0
Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentation—0
Loop Unrolled Shallow Equilibrium Regularizer (LUSER) -- A Memory-Efficient Inverse Problem Solver—0
Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion—0
Low-Dose CT Image Enhancement Using Deep Learning—0
Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoisers—0
Low-Dose CT Image Reconstruction using Vector Quantized Convolutional Autoencoder with Perceptual Loss—0
Low Dose CT Image Reconstruction With Learned Sparsifying Transform—0
Low-Dose CT via Deep CNN with Skip Connection and Network in Network—0
Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary—0
Low radiation tomographic reconstruction with and without template information—0
LTSP: Long-Term Slice Propagation for Accurate Airway Segmentation—0
LUND-PROBE -- LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset—0
LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation—0
Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images—0
Lung Cancer Detection and Classification based on Image Processing and Statistical Learning—0
Lung Cancer Detection using Co-learning from Chest CT Images and Clinical Demographics—0
Lung CT Imaging Sign Classification through Deep Learning on Small Data—0
Lung-DETR: Deformable Detection Transformer for Sparse Lung Nodule Anomaly Detection—0
CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans—0
Lung Segmentation and Nodule Detection in Computed Tomography Scan using a Convolutional Neural Network Trained Adversarially using Turing Test Loss—0
μ^2Tokenizer: Differentiable Multi-Scale Multi-Modal Tokenizer for Radiology Report Generation—0
MACE CT Reconstruction for Modular Material Decomposition from Energy Resolving Photon-Counting Data—0
Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction—0
Machine Learning Applications in Traumatic Brain Injury: A Spotlight on Mild TBI—0
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