Computed Tomography (CT)
295 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
Most implemented papers
Diagnostic Classification Of Lung Nodules Using 3D Neural Networks
Lung cancer is the leading cause of cancer-related death worldwide.
DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection
Recently deep learning has been witnessing widespread adoption in various medical image applications.
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
We demonstrate the model's clinical applicability by assessing its performance on a test set of 21 CT scans from clinical practice, each with the 21 OARs segmented by two independent experts.
False Positive Reduction in Lung Computed Tomography Images using Convolutional Neural Networks
3D CNNs are preferred over 2D CNNs because data are in 3D, and 2D convolutional operations may result in information loss.
DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection
ymli39/DeepSEED-3D-ConvNets-for-Pulmonary-Nodule-Detection
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Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans.
A Cone-Beam X-Ray CT Data Collection designed for Machine Learning
Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction.
ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
liaohaofu/adn
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Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training.
Tapering Analysis of Airways with Bronchiectasis
We propose a simple measurement of tapering along the airways to diagnose and monitor bronchiectasis.
An End-to-end Deep Learning Approach for Landmark Detection and Matching in Medical Images
We tested the approach on 22, 206 pairs of 2D slices with varying levels of intensity, affine, and elastic transformations.
COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images
Pre-training with a dataset of similar nature further improved accuracy to 98. 3% and specificity to 98. 6%.
