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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 801–850 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
Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography—0
Distant Domain Transfer Learning for Medical Imaging—0
Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization—0
Spatiotemporal tomography based on scattered multiangular signals and its application for resolving evolving clouds using moving platformsCode0
COVID-CLNet: COVID-19 Detection with Compressive Deep Learning Approaches—0
Explaining Predictions of Deep Neural Classifier via Activation Analysis—0
Generation of annotated multimodal ground truth datasets for abdominal medical image registration—0
Sparse-View Spectral CT Reconstruction Using Deep Learning—0
Unsupervised domain adaptation based COVID-19 CT infection segmentation network—0
Crowdsourcing Airway Annotations in Chest Computed Tomography ImagesCode0
ResCovNet: A Deep Learning-Based Architecture For COVID-19 Detection From Chest CT Scan Images—0
Disassemblable Fieldwork CT Scanner Using a 3D-printed Calibration Phantom—0
Classification of COVID-19 in Chest CT Images using Convolutional Support Vector Machines—0
Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images—0
Pristine annotations-based multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19—0
Patch-based field-of-view matching in multi-modal images for electroporation-based ablations—0
Self-Supervised Learning for Biological Sample Localization in 3D Tomographic Images—0
COVID-FACT: A Fully-Automated Capsule Network-based Framework for Identification of COVID-19 Cases from Chest CT scansCode0
CT-CAPS: Feature Extraction-based Automated Framework for COVID-19 Disease Identification from Chest CT Scans using Capsule NetworksCode0
ProCAN: Progressive Growing Channel Attentive Non-Local Network for Lung Nodule Classification—0
Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography—0
Deep Sequential Learning for Cervical Spine Fracture Detection on Computed Tomography Imaging—0
Automated triage of COVID-19 from various lung abnormalities using chest CT features—0
Deep Multi-path Network Integrating Incomplete Biomarker and Chest CT Data for Evaluating Lung Cancer Risk—0
Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study—0
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