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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 726–750 of 1207 papers

TitleStatusHype
Transformers for CT Reconstruction From Monoplanar and Biplanar Radiographs—0
Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network—0
Quantifying uncertainty in lung cancer segmentation with foundation models applied to mixed-domain datasets—0
TSUBF-Net: Trans-Spatial UNet-like Network with Bi-direction Fusion for Segmentation of Adenoid Hypertrophy in CT—0
TV-regularized CT Reconstruction and Metal Artifact Reduction Using Inequality Constraints with Preconditioning—0
Two Stage Segmentation of Cervical Tumors using PocketNet—0
Sequential Diffusion-Guided Deep Image Prior For Medical Image Reconstruction—0
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data—0
UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields—0
Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz—0
Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing—0
Underestimation of lung regions on chest X-ray segmentation masks assessed by comparison with total lung volume evaluated on computed tomography—0
UNet-3D with Adaptive TverskyCE Loss for Pancreas Medical Image Segmentation—0
U-Net Based Architecture for an Improved Multiresolution Segmentation in Medical Images—0
U-Net in Medical Image Segmentation: A Review of Its Applications Across Modalities—0
Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction—0
Unlocking Robust Segmentation Across All Age Groups via Continual Learning—0
Unlocking the Potential of Early Epochs: Uncertainty-aware CT Metal Artifact Reduction—0
Unsupervised Acute Intracranial Hemorrhage Segmentation with Mixture Models—0
Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection—0
Unsupervised CT Metal Artifact Learning using Attention-guided beta-CycleGAN—0
Unsupervised denoising for sparse multi-spectral computed tomography—0
Unsupervised Sparse-view Backprojection via Convolutional and Spatial Transformer Networks—0
UnWave-Net: Unrolled Wavelet Network for Compton Tomography Image Reconstruction—0
User lung cancer classification using efficientnet from ct scan images—0
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