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Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses

Radiology, 2019-08, Vol.292 (2), p.365-373

2019

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Deep Learning in Medical Imaging: General Overview

Korean Journal of Radiology, 2017, 18(4), , pp.570-584

2017

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Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network

Scientific reports, 2020-04, Vol.10 (1), p.6204-6204, Article 6204

2020

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Assessment of Luminal and Basal Phenotypes in Bladder Cancer

Scientific reports, 2020-06, Vol.10 (1), p.9743, Article 9743

2020

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Deep learning–based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC

European radiology, 2021-09, Vol.31 (9), p.7047-7057

2021

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Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net

Medical image analysis, 2019-01, Vol.51, p.13-20

2019

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Machine Learning-Based Automatic Rating for Cardinal Symptoms of Parkinson Disease

Neurology, 2021-03, Vol.96 (13), p.e1761

2021

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Fully Automatic Coronary Calcium Score Software Empowered by Artificial Intelligence Technology: Validation Study Using Three CT Cohorts

Korean Journal of Radiology, 2021, 22(11), , pp.1764-1776

2021

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Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer

Korean Journal of Radiology, 2019, 20(10), , pp.1431-1440

2019

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Outcome prediction in resectable lung adenocarcinoma patients: value of CT radiomics

European radiology, 2020-09, Vol.30 (9), p.4952-4963

2020

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Fully automated segmentation of cartilage from the MR images of knee using a multi‐atlas and local structural analysis method

Medical physics (Lancaster), 2014-09, Vol.41 (9), p.092303-n/a

2014

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Prognostic value of radiomic analysis of iodine overlay maps from dual-energy computed tomography in patients with resectable lung cancer

European radiology, 2019-02, Vol.29 (2), p.915-923

2019

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Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation

PLoS medicine, 2018-11, Vol.15 (11), p.e1002693-e1002693

2018

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CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network

Korean Journal of Radiology, 2019, 20(2), , pp.295-303

2019

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A deep learning–based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation

European radiology, 2022-03, Vol.32 (3), p.1558-1569

2022

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Automated detection of vulnerable plaque in intravascular ultrasound images

Medical & biological engineering & computing, 2019-04, Vol.57 (4), p.863-876

2019

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Automated Segmentation of Left Ventricular Myocardium on Cardiac Computed Tomography Using Deep Learning

Korean Journal of Radiology, 2020, 21(6), , pp.660-669

2020

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Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions

Journal of the American Heart Association, 2019-02, Vol.8 (4), p.e011685-e011685

2019

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ZOZI-Seg: A transformer and UNet cascade network with Zoom-Out and Zoom-In scheme for aortic dissection segmentation in enhanced CT images

Computers in biology and medicine, 2024-06, Vol.175, p.108494-108494, Article 108494

2024

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Hybrid Airway Segmentation Using Multi-Scale Tubular Structure Filters and Texture Analysis on 3D Chest CT Scans

Journal of digital imaging, 2019-10, Vol.32 (5), p.779-792

2019

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Deep Learning-Based Lumen and Vessel Segmentation of Intravascular Ultrasound Images in Coronary Artery Disease

Korean Circulation Journal, 2024, 54(1), , pp.30-39

2024

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CycleMorph: Cycle consistent unsupervised deformable image registration

Medical image analysis, 2021-07, Vol.71, p.102036-102036, Article 102036

2021

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Detection of optical coherence tomography-defined thin-cap fibroatheroma in the coronary artery using deep learning

EuroIntervention, 2020-08, Vol.16 (5), p.404-412

2020

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Deep chest X‐ray: Detection and classification of lesions based on deep convolutional neural networks

International journal of imaging systems and technology, 2021-03, Vol.31 (1), p.72-81

2021

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Informatics in radiology: dual-energy electronic cleansing for fecal-tagging CT colonography

Radiographics, 2013-05, Vol.33 (3), p.891-912

2013

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Deep Learning-based Image Conversion of CT Reconstruction Kernels Improves Radiomics Reproducibility for Pulmonary Nodules or Masses

Deep Learning in Medical Imaging: General Overview

Abdominal multi-organ auto-segmentation using 3D-patch-based deep convolutional neural network

Assessment of Luminal and Basal Phenotypes in Bladder Cancer

Deep learning–based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC

Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net

Machine Learning-Based Automatic Rating for Cardinal Symptoms of Parkinson Disease

Fully Automatic Coronary Calcium Score Software Empowered by Artificial Intelligence Technology: Validation Study Using Three CT Cohorts

Deep Learning Algorithm for Reducing CT Slice Thickness: Effect on Reproducibility of Radiomic Features in Lung Cancer

Outcome prediction in resectable lung adenocarcinoma patients: value of CT radiomics

Fully automated segmentation of cartilage from the MR images of knee using a multi‐atlas and local structural analysis method

Prognostic value of radiomic analysis of iodine overlay maps from dual-energy computed tomography in patients with resectable lung cancer

Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation

CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network

A deep learning–based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation

Automated detection of vulnerable plaque in intravascular ultrasound images

Automated Segmentation of Left Ventricular Myocardium on Cardiac Computed Tomography Using Deep Learning

Angiography-Based Machine Learning for Predicting Fractional Flow Reserve in Intermediate Coronary Artery Lesions

ZOZI-Seg: A transformer and UNet cascade network with Zoom-Out and Zoom-In scheme for aortic dissection segmentation in enhanced CT images

Hybrid Airway Segmentation Using Multi-Scale Tubular Structure Filters and Texture Analysis on 3D Chest CT Scans

Deep Learning-Based Lumen and Vessel Segmentation of Intravascular Ultrasound Images in Coronary Artery Disease

CycleMorph: Cycle consistent unsupervised deformable image registration

Detection of optical coherence tomography-defined thin-cap fibroatheroma in the coronary artery using deep learning

Deep chest X‐ray: Detection and classification of lesions based on deep convolutional neural networks

Informatics in radiology: dual-energy electronic cleansing for fecal-tagging CT colonography

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