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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

Showing 526550 of 1207 papers

TitleStatusHype
3D-Morphomics, Morphological Features on CT scans for lung nodule malignancy diagnosis0
Quad-Net: Quad-domain Network for CT Metal Artifact ReductionCode1
Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study0
Large-Kernel Attention for 3D Medical Image Segmentation0
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT0
Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension PerspectiveCode1
Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives0
Domain Knowledge Driven 3D Dose Prediction Using Moment-Based Loss FunctionCode1
Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets0
Adaptive GLCM sampling for transformer-based COVID-19 detection on CT0
Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scans0
COVID-19 Detection Using Transfer Learning Approach from Computed Tomography ImagesCode0
Classical and learned MR to pseudo-CT mappings for accurate transcranial ultrasound simulation0
Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs0
3D unsupervised anomaly detection and localization through virtual multi-view projection and reconstruction: Clinical validation on low-dose chest computed tomographyCode0
Near-Exact Recovery for Tomographic Inverse Problems via Deep LearningCode1
GradICON: Approximate Diffeomorphisms via Gradient Inverse ConsistencyCode1
Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CTCode1
ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation0
Dual Windows Are Significant: Learning from Mediastinal Window and Focusing on Lung Window0
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT ImagingCode1
Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT ScansCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
Recursive Deformable Image Registration Network with Mutual Attention0
R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation0
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