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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 751–775 of 1207 papers

TitleStatusHype
Using Deep Learning-based Features Extracted from CT scans to Predict Outcomes in COVID-19 Patients—0
Using the Order of Tomographic Slices as a Prior for Neural Networks Pre-Training—0
Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives—0
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge—0
Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images—0
Validation of Subject-Specific Knee Models from In Vivo Measurements—0
Validation of Tsallis Entropy In Inter-Modality Neuroimage Registration—0
Virtual vs. Reality: External Validation of COVID-19 Classifiers using XCAT Phantoms for Chest Computed Tomography—0
3D Lymphoma Segmentation on PET/CT Images via Multi-Scale Information Fusion with Cross-Attention—0
Visualization of fully connected layer weights in deep learning CT reconstruction—0
Visual Transformer with Statistical Test for COVID-19 Classification—0
Virtual Lung Screening Trial (VLST): An In Silico Study Inspired by the National Lung Screening Trial for Lung Cancer Detection—0
Volume Calculation of CT lung Lesions based on Halton Low-discrepancy Sequences—0
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation—0
Weakly supervised alignment and registration of MR-CT for cervical cancer radiotherapy—0
Weakly Supervised Geodesic Segmentation of Egyptian Mummy CT Scans—0
Weakly-Supervised Lesion Segmentation on CT Scans using Co-Segmentation—0
Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss—0
Weakly Supervised Volumetric Image Segmentation with Deformed Templates—0
Weaving Attention U-net: A Novel Hybrid CNN and Attention-based Method for Organs-at-risk Segmentation in Head and Neck CT Images—0
When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT—0
Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin—0
Whole-brain substitute CT generation using Markov random field mixture models—0
X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning—0
X2CT-FLOW: Maximum a posteriori reconstruction using a progressive flow-based deep generative model for ultra sparse-view computed tomography in ultra low-dose protocols—0
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