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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 401–425 of 1207 papers

TitleStatusHype
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation—0
MACE CT Reconstruction for Modular Material Decomposition from Energy Resolving Photon-Counting Data—0
Can Generative AI Support Patients' & Caregivers' Informational Needs? Towards Task-Centric Evaluation Of AI Systems—0
CAFCT-Net: A CNN-Transformer Hybrid Network with Contextual and Attentional Feature Fusion for Liver Tumor Segmentation—0
Integer Optimization of CT Trajectories using a Discrete Data Completeness Formulation—0
Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier SeriesCode0
Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification—0
Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Images—0
Machine Learning Applications in Traumatic Brain Injury: A Spotlight on Mild TBI—0
Web Diagnosis for COVID-19 and Pneumonia Based on Computed Tomography Scans and X-raysCode0
Cadmium Zinc Telluride (CZT) photon counting detector Characterisation for soft tissue imaging—0
Deep Radon Prior: A Fully Unsupervised Framework for Sparse-View CT Reconstruction—0
3DGR-CT: Sparse-View CT Reconstruction with a 3D Gaussian Representation—0
Texture Matching GAN for CT Image Enhancement—0
COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images—0
Exploring 3D U-Net Training Configurations and Post-Processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge—0
Class-Discriminative Attention Maps for Vision Transformers—0
Convolutional Neural Networks for Segmentation of Malignant Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance)—0
An Ensemble of 2.5D ResUnet Based Models for Segmentation for Kidney and Masses—0
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines—0
View it like a radiologist: Shifted windows for deep learning augmentation of CT imagesCode0
Lightweight Framework for Automated Kidney Stone Detection using coronal CT images—0
Enhancing mTBI Diagnosis with Residual Triplet Convolutional Neural Network Using 3D CT—0
Lung cancer detection from thoracic CT scans using an ensemble of deep learning modelsCode0
Liver Tumor Prediction with Advanced Attention Mechanisms Integrated into a Depth-Based Variant Search Algorithm—0
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