radiomics matlab package Search Results


standardized environment for radiomics analysis (sera) package  (MathWorks Inc)
90
MathWorks Inc standardized environment for radiomics analysis (sera) package
Standardized Environment For Radiomics Analysis (Sera) Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/standardized environment for radiomics analysis (sera) package/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
standardized environment for radiomics analysis (sera) package - by Bioz Stars, 2026-03
90/100 stars
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radiomics matlab package  (MathWorks Inc)
90
MathWorks Inc radiomics matlab package
Radiomics Matlab Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/radiomics matlab package/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
radiomics matlab package - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
radiomics analysis package  (MathWorks Inc)
90
MathWorks Inc radiomics analysis package
Radiomics Analysis Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/radiomics analysis package/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
radiomics analysis package - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
matlab radiomic package  (MathWorks Inc)
90
MathWorks Inc matlab radiomic package
Schema for lung cancer segmentation, <t>radiomic</t> feature extraction and predictive modeling. (A) Representative CT images from small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) showing tumor segmentation. (B) Illustrations of radiomic feature extraction for texture, shape, and intensity. (C) Decision of SCCL/NSCLC classification (upper panel) with the receiver operating characteristic (ROC) curves (middle panel) and the heat map of radiomic features (lower panel).
Matlab Radiomic Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/matlab radiomic package/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
matlab radiomic package - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
matlab-based radiomics tools package  (MathWorks Inc)
90
MathWorks Inc matlab-based radiomics tools package
Schema for lung cancer segmentation, <t>radiomic</t> feature extraction and predictive modeling. (A) Representative CT images from small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) showing tumor segmentation. (B) Illustrations of radiomic feature extraction for texture, shape, and intensity. (C) Decision of SCCL/NSCLC classification (upper panel) with the receiver operating characteristic (ROC) curves (middle panel) and the heat map of radiomic features (lower panel).
Matlab Based Radiomics Tools Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/matlab-based radiomics tools package/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
matlab-based radiomics tools package - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
radiomics tool package matlab 2024a  (MathWorks Inc)
90
MathWorks Inc radiomics tool package matlab 2024a
Schema for lung cancer segmentation, <t>radiomic</t> feature extraction and predictive modeling. (A) Representative CT images from small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) showing tumor segmentation. (B) Illustrations of radiomic feature extraction for texture, shape, and intensity. (C) Decision of SCCL/NSCLC classification (upper panel) with the receiver operating characteristic (ROC) curves (middle panel) and the heat map of radiomic features (lower panel).
Radiomics Tool Package Matlab 2024a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/radiomics tool package matlab 2024a/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
radiomics tool package matlab 2024a - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
standardised environment for radiomics analysis package  (MathWorks Inc)
90
MathWorks Inc standardised environment for radiomics analysis package
Schema for lung cancer segmentation, <t>radiomic</t> feature extraction and predictive modeling. (A) Representative CT images from small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) showing tumor segmentation. (B) Illustrations of radiomic feature extraction for texture, shape, and intensity. (C) Decision of SCCL/NSCLC classification (upper panel) with the receiver operating characteristic (ROC) curves (middle panel) and the heat map of radiomic features (lower panel).
Standardised Environment For Radiomics Analysis Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/standardised environment for radiomics analysis package/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
standardised environment for radiomics analysis package - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
package of radiomics analysis  (MathWorks Inc)
90
MathWorks Inc package of radiomics analysis
Comparison between <t> radiomics </t> model and senior radiologists in training set.
Package Of Radiomics Analysis, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/package of radiomics analysis/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
package of radiomics analysis - by Bioz Stars, 2026-03
90/100 stars
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matlab 2017b  (MathWorks Inc)
90
MathWorks Inc matlab 2017b
Comparison between <t> radiomics </t> model and senior radiologists in training set.
Matlab 2017b, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/matlab 2017b/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
matlab 2017b - by Bioz Stars, 2026-03
90/100 stars
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custom-built matlab software package  (MathWorks Inc)
90
MathWorks Inc custom-built matlab software package
Comparison between <t> radiomics </t> model and senior radiologists in training set.
Custom Built Matlab Software Package, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/custom-built matlab software package/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
custom-built matlab software package - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier

Image Search Results


Schema for lung cancer segmentation, radiomic feature extraction and predictive modeling. (A) Representative CT images from small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) showing tumor segmentation. (B) Illustrations of radiomic feature extraction for texture, shape, and intensity. (C) Decision of SCCL/NSCLC classification (upper panel) with the receiver operating characteristic (ROC) curves (middle panel) and the heat map of radiomic features (lower panel).

Journal: Frontiers in Oncology

Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

doi: 10.3389/fonc.2020.00593

Figure Lengend Snippet: Schema for lung cancer segmentation, radiomic feature extraction and predictive modeling. (A) Representative CT images from small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) showing tumor segmentation. (B) Illustrations of radiomic feature extraction for texture, shape, and intensity. (C) Decision of SCCL/NSCLC classification (upper panel) with the receiver operating characteristic (ROC) curves (middle panel) and the heat map of radiomic features (lower panel).

Article Snippet: We extracted the tumor textural features using the MATLAB radiomic package ( https://github.com/mvallieres/radiomics ) and the textural analysis formula ( ).

Techniques:

The nnet architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Journal: Frontiers in Oncology

Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

doi: 10.3389/fonc.2020.00593

Figure Lengend Snippet: The nnet architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Article Snippet: We extracted the tumor textural features using the MATLAB radiomic package ( https://github.com/mvallieres/radiomics ) and the textural analysis formula ( ).

Techniques:

The top 20 features selected from the radiomic data set (total 1,731 features) for the small cell lung cancer (SCLC) / non-small-cell lung cancer (NSCLC) classification. (A) Measurements for top 20 features. Each feature (matrix row) consisted of 35 SCLC measurements (index 1:35) and 34 NSCLC measurements (index 36:69). Each feature vector was normalized by max=1. (B) Mutual information map for the top 20 features. A large mutual information value indicated a high redundancy between the features.

Journal: Frontiers in Oncology

Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

doi: 10.3389/fonc.2020.00593

Figure Lengend Snippet: The top 20 features selected from the radiomic data set (total 1,731 features) for the small cell lung cancer (SCLC) / non-small-cell lung cancer (NSCLC) classification. (A) Measurements for top 20 features. Each feature (matrix row) consisted of 35 SCLC measurements (index 1:35) and 34 NSCLC measurements (index 36:69). Each feature vector was normalized by max=1. (B) Mutual information map for the top 20 features. A large mutual information value indicated a high redundancy between the features.

Article Snippet: We extracted the tumor textural features using the MATLAB radiomic package ( https://github.com/mvallieres/radiomics ) and the textural analysis formula ( ).

Techniques: Plasmid Preparation

Comparison between radiomics model and senior radiologists in training set.

Journal: Frontiers in Oncology

Article Title: Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study

doi: 10.3389/fonc.2022.856605

Figure Lengend Snippet: Comparison between radiomics model and senior radiologists in training set.

Article Snippet: The radiomics parameters were extracted using a MATLAB (R2020a) package of radiomics analysis from Github (github.com/mvallieres/radiomics), and the statistical modeling was performed by R software (version 3.6.2).

Techniques: Comparison

Comparison between radiomics model and senior radiologists in test set.

Journal: Frontiers in Oncology

Article Title: Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study

doi: 10.3389/fonc.2022.856605

Figure Lengend Snippet: Comparison between radiomics model and senior radiologists in test set.

Article Snippet: The radiomics parameters were extracted using a MATLAB (R2020a) package of radiomics analysis from Github (github.com/mvallieres/radiomics), and the statistical modeling was performed by R software (version 3.6.2).

Techniques: Comparison

Area under the curve between the radiomics model and the senior radiologists in the test set.

Journal: Frontiers in Oncology

Article Title: Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study

doi: 10.3389/fonc.2022.856605

Figure Lengend Snippet: Area under the curve between the radiomics model and the senior radiologists in the test set.

Article Snippet: The radiomics parameters were extracted using a MATLAB (R2020a) package of radiomics analysis from Github (github.com/mvallieres/radiomics), and the statistical modeling was performed by R software (version 3.6.2).

Techniques:

Receiver operating characteristic between the radiomics model and radiologists in the whole set.

Journal: Frontiers in Oncology

Article Title: Ultrasound-Based Radiomics Can Classify the Etiology of Cervical Lymphadenopathy: A Multi-Center Retrospective Study

doi: 10.3389/fonc.2022.856605

Figure Lengend Snippet: Receiver operating characteristic between the radiomics model and radiologists in the whole set.

Article Snippet: The radiomics parameters were extracted using a MATLAB (R2020a) package of radiomics analysis from Github (github.com/mvallieres/radiomics), and the statistical modeling was performed by R software (version 3.6.2).

Techniques: