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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 476–500 of 1207 papers

TitleStatusHype
Generative Modeling in Sinogram Domain for Sparse-view CT ReconstructionCode1
Mediastinal Lymph Node Detection and Segmentation Using Deep Learning—0
On the impact of incorporating task-information in learning-based image denoising—0
Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction—0
Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19Code1
Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image GenerationCode2
Active CT Reconstruction with a Learned Sampling Policy—0
On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT SettingCode1
Unsupervised denoising for sparse multi-spectral computed tomography—0
Self-supervised Physics-based Denoising for Computed Tomography—0
Data-Driven Computational Imaging for Scientific DiscoveryCode0
Spatio-Temporal Hybrid Fusion of CAE and SWIn Transformers for Lung Cancer Malignancy Prediction—0
Fully Automated Deep Learning-enabled Detection for Hepatic Steatosis on Computed Tomography: A Multicenter International Validation Study—0
Residual Back Projection With Untrained Neural Networks—0
Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency GuidanceCode0
Adaptive Contrastive Learning with Dynamic Correlation for Multi-Phase Organ SegmentationCode0
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time AugmentationCode0
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT ScansCode1
NSCGCN: A novel deep GCN model to diagnosis COVID-19—0
Loop Unrolled Shallow Equilibrium Regularizer (LUSER) -- A Memory-Efficient Inverse Problem Solver—0
Evaluating the Performance of StyleGAN2-ADA on Medical Images—0
FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT ImagesCode1
COVID-19 Detection Using Segmentation, Region Extraction and Classification PipelineCode0
LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation—0
Evaluation of importance estimators in deep learning classifiers for Computed Tomography—0
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