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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 801–825 of 1207 papers

TitleStatusHype
Classification of COVID-19 X-ray Images Using a Combination of Deep and Handcrafted Features—0
Covid-19 classification with deep neural network and belief functions—0
Deep-Learning Driven Noise Reduction for Reduced Flux Computed Tomography—0
Self-Supervised Learning for Segmentation—0
A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis—0
A patient-specific approach for quantitative and automatic analysis of computed tomography images in lung disease: application to COVID-19 patients—0
Generative Adversarial U-Net for Domain-free Medical Image Augmentation—0
A New Weighting Scheme for Fan-beam and Circle Cone-beam CT Reconstructions—0
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning—0
4D Cloud Scattering TomographyCode0
IntraTomo: Self-Supervised Learning-Based Tomography via Sinogram Synthesis and PredictionCode0
Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation—0
Screening COVID-19 Based on CT/CXR Images & Building a Publicly Available CT-scan Dataset of COVID-19—0
Multi-Contrast Computed Tomography Healthy Kidney Atlas—0
Contraband Materials Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery—0
Automated segmentation of an intensity calibration phantom in clinical CT images using a convolutional neural networkCode0
A new semi-supervised self-training method for lung cancer prediction—0
CT Film Recovery via Disentangling Geometric Deformation and Illumination Variation: Simulated Datasets and Deep Models—0
CT Super Resolution via Zero Shot Learning—0
Automated 3D cephalometric landmark identification using computerized tomography—0
Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT SlicesCode0
Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks—0
LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CTCode0
CHS-Net: A Deep learning approach for hierarchical segmentation of COVID-19 infected CT images—0
AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre studyCode0
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