UB Paderborn / Katalog / Suche / in CDI

1 – 25 von 68

Sortiert nach

Computational Radiomics System to Decode the Radiographic Phenotype

Cancer research (Chicago, Ill.), 2017-11, Vol.77 (21), p.e104-e107

2017

Link zum Volltext

Verfügbarkeit prüfen

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

Cancer research (Chicago, Ill.), 2017-07, Vol.77 (14), p.3922-3930

2017

Link zum Volltext

Verfügbarkeit prüfen

Data Analysis Strategies in Medical Imaging

Clinical cancer research, 2018-08, Vol.24 (15), p.3492-3499

2018

Link zum Volltext

Verfügbarkeit prüfen

Machine Learning methods for Quantitative Radiomic Biomarkers

Scientific reports, 2015-08, Vol.5 (1), p.13087, Article 13087

2015

Link zum Volltext

Verfügbarkeit prüfen

Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging

Clinical cancer research, 2019-06, Vol.25 (11), p.3266-3275

2019

Link zum Volltext

Verfügbarkeit prüfen

Defining the biological basis of radiomic phenotypes in lung cancer

eLife, 2017-07, Vol.6

2017

Link zum Volltext

Verfügbarkeit prüfen

Deep convolutional neural networks to predict cardiovascular risk from computed tomography

Nature communications, 2021-01, Vol.12 (1), p.715-715, Article 715

2021

Link zum Volltext

Verfügbarkeit prüfen

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

PLoS medicine, 2018-11, Vol.15 (11), p.e1002711

2018

Link zum Volltext

Verfügbarkeit prüfen

Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology

Frontiers in oncology, 2016-03, Vol.6, p.71-71

2016

Link zum Volltext

Verfügbarkeit prüfen

Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer

Frontiers in oncology, 2015-12, Vol.5, p.272-272

2015

Link zum Volltext

Verfügbarkeit prüfen

Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer

Scientific reports, 2015-06, Vol.5 (1), p.11044-11044, Article 11044

2015

Link zum Volltext

Verfügbarkeit prüfen

Robust Radiomics feature quantification using semiautomatic volumetric segmentation

PloS one, 2014-07, Vol.9 (7), p.e102107-e102107

2014

Link zum Volltext

Verfügbarkeit prüfen

Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR

Scientific reports, 2017-07, Vol.7 (1), p.5301-9, Article 5301

2017

Link zum Volltext

Verfügbarkeit prüfen

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT

PloS one, 2017-01, Vol.12 (1), p.e0169172-e0169172

2017

Link zum Volltext

Verfügbarkeit prüfen

Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability

Acta oncologica, 2013-10, Vol.52 (7), p.1391-1397

2013

Link zum Volltext

Verfügbarkeit prüfen

Volumetric CT-based segmentation of NSCLC using 3D-Slicer

Scientific reports, 2013-12, Vol.3 (1), p.3529-3529, Article 3529

2013

Link zum Volltext

Verfügbarkeit prüfen

Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Scientific reports, 2017-06, Vol.7 (1), p.3519-11, Article 3519

2017

Link zum Volltext

Verfügbarkeit prüfen

Radiographic prediction of meningioma grade by semantic and radiomic features

PloS one, 2017-11, Vol.12 (11), p.e0187908-e0187908

2017

Link zum Volltext

Verfügbarkeit prüfen

Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation

PloS one, 2017-06, Vol.12 (6), p.e0178944-e0178944

2017

Link zum Volltext

Verfügbarkeit prüfen

FPGA implementation of hardware efficient adaptive filter robust to impulsive noise

IET computers & digital techniques, 2017-05, Vol.11 (3), p.107-116

2017

Link zum Volltext

Verfügbarkeit prüfen

Author Correction: Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR

Scientific reports, 2018-02, Vol.8 (1), p.2589-1, Article 2589

2018

Link zum Volltext

Verfügbarkeit prüfen

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Nature communications, 2014-06, Vol.5 (1), p.4006-4006, Article 4006

2014

Link zum Volltext

Verfügbarkeit prüfen

LoRa based Architecture for Fault Localization in Transmission Lines

International journal of computer applications, 2020-02, Vol.177 (39), p.16-20

2020

Link zum Volltext

Verfügbarkeit prüfen

Machine (Deep) Learning Methods for Image Processing and Radiomics

IEEE transactions on radiation and plasma medical sciences, 2019-03, Vol.3 (2), p.104-108

2019

Link zum Volltext
IEEE_Xplore

1 – 25 von 68

Suche als RSS-Feed

Suchergebnisse filtern

Filter anzeigen

Nur zeigen

Geprüft (Peer-Review)
(52)
Mit Volltextzugriff
(67)

Publikationsform

Sprache

Englisch
(68)
Japanisch
(14)

Erscheinungsjahr

n.n

Thema

Science & Technology
(43)
Life Sciences & Biomedicine
(25)
Humans
(23)
Oncology
(16)
Multidisciplinary Sciences
(12)
Radiomics
(12)
Science & Technology - Other Topics
(12)
Female
(11)
Lung Cancer
(11)
Male
(11)
Cancer
(10)
Computed Tomography
(10)
Biomarkers
(9)
Medical Imaging
(9)
Tumors
(9)
Aged
(8)
Algorithms
(8)
Lung Neoplasms - Diagnostic Imaging
(8)
Machine Learning
(8)
Neural Networks
(8)

15 weitere anzeigen...

Weniger anzeigen...

Neu hinzugefügt

... seit 90 Tagen
(1)

Menü

Weitere Dienste

Einstellungen

Computational Radiomics System to Decode the Radiographic Phenotype

Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer

Data Analysis Strategies in Medical Imaging

Machine Learning methods for Quantitative Radiomic Biomarkers

Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging

Defining the biological basis of radiomic phenotypes in lung cancer

Deep convolutional neural networks to predict cardiovascular risk from computed tomography

Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study

Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology

Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer

Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer

Robust Radiomics feature quantification using semiautomatic volumetric segmentation

Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR

Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT

Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability

Volumetric CT-based segmentation of NSCLC using 3D-Slicer

Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

Radiographic prediction of meningioma grade by semantic and radiomic features

Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation

FPGA implementation of hardware efficient adaptive filter robust to impulsive noise

122 Quantification of tumor infiltrating lymphocytes (TILs) from pathology slides reflects molecular immune phenotypes

Author Correction: Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

LoRa based Architecture for Fault Localization in Transmission Lines

Machine (Deep) Learning Methods for Image Processing and Radiomics

Suchergebnisse filtern