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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 451–475 of 1207 papers

TitleStatusHype
Synthetic CT Generation via Variant Invertible Network for All-digital Brain PET Attenuation CorrectionCode0
Self-distilled Masked Attention guided masked image modeling with noise Regularized Teacher (SMART) for medical image analysis—0
Predicting Lung Cancer's Metastats' Locations Using Bioclinical Model—0
Development of a Deep Learning Method to Identify Acute Ischemic Stroke Lesions on Brain CT—0
Abdominal multi-organ segmentation in CT using Swinunter—0
Attention and Pooling based Sigmoid Colon Segmentation in 3D CT images—0
Autopet Challenge 2023: nnUNet-based whole-body 3D PET-CT Tumour SegmentationCode0
Solving Low-Dose CT Reconstruction via GAN with Local Coherence—0
Design of Novel Loss Functions for Deep Learning in X-ray CT—0
Inter-vendor harmonization of Computed Tomography (CT) reconstruction kernels using unpaired image translation—0
Auto-Lesion Segmentation with a Novel Intensity Dark Channel Prior for COVID-19 Detection—0
Interpretable 3D Multi-Modal Residual Convolutional Neural Network for Mild Traumatic Brain Injury Diagnosis—0
Scribble-based 3D Multiple Abdominal Organ Segmentation via Triple-branch Multi-dilated Network with Pixel- and Class-wise Consistency—0
Deep conditional generative models for longitudinal single-slice abdominal computed tomography harmonizationCode0
M3Dsynth: A dataset of medical 3D images with AI-generated local manipulationsCode0
Limited-Angle Tomography Reconstruction via Deep End-To-End Learning on Synthetic DataCode0
A Localization-to-Segmentation Framework for Automatic Tumor Segmentation in Whole-Body PET/CT ImagesCode0
An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports—0
User lung cancer classification using efficientnet from ct scan images—0
Multi-stage Deep Learning Artifact Reduction for Pallel-beam Computed Tomography—0
Attention-based CT Scan Interpolation for Lesion Segmentation of Colorectal Liver Metastases—0
High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach—0
PECon: Contrastive Pretraining to Enhance Feature Alignment between CT and EHR Data for Improved Pulmonary Embolism DiagnosisCode0
Virtual imaging trials improved the transparency and reliability of AI systems in COVID-19 imagingCode0
Two-and-a-half Order Score-based Model for Solving 3D Ill-posed Inverse ProblemsCode0
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