Computed Tomography (CT)

114 papers with code • 0 benchmarks • 13 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 )

Greatest papers with code

Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

fizyr/keras-retinanet 5 Jun 2019

We propose a highly accurate and efficient one-stage lesion detector, by re-designing a RetinaNet to meet the particular challenges in medical imaging.

Computed Tomography (CT) Region Proposal +1

UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation

MrGiovanni/Nested-UNet 11 Dec 2019

The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).

 Ranked #1 on Medical Image Segmentation on EM (IoU metric)

Computed Tomography (CT) Electron Microscopy +3

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

UCSD-AI4H/COVID-CT 30 Mar 2020

Using this dataset, we develop diagnosis methods based on multi-task learning and self-supervised learning, that achieve an F1 of 0. 90, an AUC of 0. 98, and an accuracy of 0. 89.

Computed Tomography (CT) COVID-19 Diagnosis +2

A Reference Architecture for Plausible Threat Image Projection (TIP) Within 3D X-ray Computed Tomography Volumes

ljvmiranda921/pyswarms 15 Jan 2020

Threat Image Projection (TIP) is a technique used in X-ray security baggage screening systems that superimposes a threat object signature onto a benign X-ray baggage image in a plausible and realistic manner.

Computed Tomography (CT)

MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation

rsummers11/CADLab 12 Aug 2019

When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report.

Computed Tomography (CT)

Anatomy-specific classification of medical images using deep convolutional nets

rsummers11/CADLab 15 Apr 2015

We show that a data augmentation approach can help to enrich the data set and improve classification performance.

Computed Tomography (CT) Data Augmentation +2

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes

neheller/kits19 31 Mar 2019

The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment.

Computed Tomography (CT) Decision Making +1

Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images

HzFu/COVID19_imaging_AI_paper_list 22 Apr 2020

Coronavirus Disease 2019 (COVID-19) spread globally in early 2020, causing the world to face an existential health crisis.

Camouflaged Object Segmentation Camouflage Segmentation +1