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in-house software based on matlab platform version 9.3 matlab r2017b  (MathWorks Inc)


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    Structured Review

    MathWorks Inc in-house software based on matlab platform version 9.3 matlab r2017b
    Imaging and <t> radiomics </t> methodology.
    In House Software Based On Matlab Platform Version 9.3 Matlab R2017b, 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/in-house software based on matlab platform version 9.3 matlab r2017b/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    in-house software based on matlab platform version 9.3 matlab r2017b - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease"

    Article Title: A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease

    Journal: Tomography

    doi: 10.3390/tomography10090108

    Imaging and radiomics methodology.
    Figure Legend Snippet: Imaging and radiomics methodology.

    Techniques Used: Imaging, Extraction, Software, Biomarker Discovery, Construct, Activity Assay



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    Image Search Results


    Imaging and radiomics methodology.

    Journal: Tomography

    Article Title: A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease

    doi: 10.3390/tomography10090108

    Figure Lengend Snippet: Imaging and radiomics methodology.

    Article Snippet: Kafouris et al. [ ] , PET/CT using 0.14 mCi/kg 18 F-FDG , Adherence to radiomics guidelines: features extracted according to IBSI guidelines Feature extraction software: in-house software based on Matlab platform (Version 9.3, Matlab R2017b, Natick, MA, USA) , Segmentation: manual segmentation around the carotid artery wall Features extracted: first order, GLCM, GLRLM, GLSZM and NGTDM Machine learning techniques: univariate logistic regression , Performance assessment: AUC from the ROC Internal validation: bootstrapping generating 200 bootstrap samples No external validation.

    Techniques: Imaging, Extraction, Software, Biomarker Discovery, Construct, Activity Assay