Computed Tomography (CT)
292 papers with code • 0 benchmarks • 14 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Computed Tomography (CT)
Libraries
Use these libraries to find Computed Tomography (CT) models and implementationsDatasets
LUNA
PadChest
LUNA16
ChestX-ray8
LiTS17
COVID-CT
MosMedData
BIMCV COVID-19
CC-19
Latest papers
A Closer Look at Spatial-Slice Features Learning for COVID-19 Detection
Conventional Computed Tomography (CT) imaging recognition faces two significant challenges: (1) There is often considerable variability in the resolution and size of each CT scan, necessitating strict requirements for the input size and adaptability of models.
EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction
However, the choice of loss function profoundly affects the reconstructed images.
From Pixel to Cancer: Cellular Automata in Computed Tomography
AI for cancer detection encounters the bottleneck of data scarcity, annotation difficulty, and low prevalence of early tumors.
Low-dose CT Denoising with Language-engaged Dual-space Alignment
hao1635/leda
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While various deep learning methods were proposed for low-dose computed tomography (CT) denoising, they often suffer from over-smoothing, blurring, and lack of explainability.
Towards Generalizable Tumor Synthesis
Tumor synthesis enables the creation of artificial tumors in medical images, facilitating the training of AI models for tumor detection and segmentation.
Evaluating Adversarial Robustness of Low dose CT Recovery
kvgandikota/robustness-low-dose-ct
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Both classical approaches and deep networks are affected by such attacks leading to changes in the visual appearance of localized lesions, for extremely small perturbations.
ICHPro: Intracerebral Hemorrhage Prognosis Classification Via Joint-attention Fusion-based 3d Cross-modal Network
yu-deep/ich
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Intracerebral Hemorrhage (ICH) is the deadliest subtype of stroke, necessitating timely and accurate prognostic evaluation to reduce mortality and disability.
Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series
sypsyp97/Trainable-Fourier-Series
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In this study, we introduce a Fourier series-based trainable filter for computed tomography (CT) reconstruction within the filtered backprojection (FBP) framework.
MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration
Capturing voxel-wise spatial correspondence across distinct modalities is crucial for medical image analysis.
Dual-Domain Coarse-to-Fine Progressive Estimation Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT
xiongchaochen/dudocfnet-multitask
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Additionally, Computed Tomography (CT) is commonly used to derive attenuation maps ($\mu$-maps) for attenuation correction (AC) of cardiac SPECT, but it will introduce additional radiation exposure and SPECT-CT misalignments.
