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in-house algorithm matlab r2017a  (MathWorks Inc)


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    MathWorks Inc in-house algorithm matlab r2017a
    Applications of radiomics in pancreatic CT images.
    In House Algorithm Matlab R2017a, 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/product/in-house+algorithm+matlab+r2017a/pmc09408381-2-4-6
    Average 90 stars, based on 1 article reviews
    in-house algorithm matlab r2017a - by Bioz Stars, 2026-09
    90/100 stars

    Images

    1) Product Images from "Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review"

    Article Title: Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review

    Journal: Healthcare

    doi: 10.3390/healthcare10081511

    Applications of radiomics in pancreatic CT images.
    Figure Legend Snippet: Applications of radiomics in pancreatic CT images.

    Techniques Used: Software, Biomarker Discovery, Histopathology, Imaging, Standard Deviation, Functional Assay

    Applications of radiomics in pancreatic MRI images.
    Figure Legend Snippet: Applications of radiomics in pancreatic MRI images.

    Techniques Used: Biomarker Discovery, Histopathology, Software, Methylated DNA Immunoprecipitation

    Related Articles

    Biomarker Discovery:

    Article Title: Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review
    Article Snippet: Yang (1) , 2019 , LIFEx software , Differential diagnosis (MCN vs. SCN) , 91 (32 MCNs, 59 SCNs) , SW , Histopathology , PAP , Textural features: AUC 0.777 Textural features + morphological characteristics: AUC 0.893. .. Xie , 2019 , In-house algorithm (MATLAB R2017a) , Differential diagnosis (MCN vs. SCN) , 57 (31 MCNs, 26 SCNs) , SW , Radiologist , AP, PVP, DP , Radiomics model: AUC 0.989, Acc 94.7%, Sen 93.6%, Spe 96.2% Combined model (radiomics + radiological features): AUC 0.994, Acc 98.2%, Sen 96.8%, Spe 100%. .. Chen , 2021 , Analysis Kit Software (v 3.0.0.R) , Differential diagnosis (PCN vs. SCN) , 89 (31 SCNs, 30 IPMNs, 28 MCNs) , RW (63 TS, 26 VS) , Radiologist , NECT, AP, PVP , Radiomics signature NECT + AP + PVP: AUC 0.817.

    other:

    Article Title: MRI pulse sequence integration for deep-learning-based brain metastases segmentation.
    Article Snippet: © 2021 American Association of Physicists in Medicine *Work performed while author was at Department of Biomedical Data Science at Stanford University.. 1Department of Biomedical Data Science, Stanford University, Stanford, California, USA 2Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois, USA 3Department for Diagnostic Physics, Oslo University Hospital, Oslo, Norway 4Department of Radiology, Stanford University, Stanford, California, USA 5Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway 6Department of Oncology, Oslo University Hospital, Oslo, Norway



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    MathWorks Inc in-house algorithm matlab r2017a
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    Image Search Results


    Applications of radiomics in pancreatic CT images.

    Journal: Healthcare

    Article Title: Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review

    doi: 10.3390/healthcare10081511

    Figure Lengend Snippet: Applications of radiomics in pancreatic CT images.

    Article Snippet: Xie , 2019 , In-house algorithm (MATLAB R2017a) , Differential diagnosis (MCN vs. SCN) , 57 (31 MCNs, 26 SCNs) , SW , Radiologist , AP, PVP, DP , Radiomics model: AUC 0.989, Acc 94.7%, Sen 93.6%, Spe 96.2% Combined model (radiomics + radiological features): AUC 0.994, Acc 98.2%, Sen 96.8%, Spe 100%.

    Techniques: Software, Biomarker Discovery, Histopathology, Imaging, Standard Deviation, Functional Assay

    Applications of radiomics in pancreatic MRI images.

    Journal: Healthcare

    Article Title: Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review

    doi: 10.3390/healthcare10081511

    Figure Lengend Snippet: Applications of radiomics in pancreatic MRI images.

    Article Snippet: Xie , 2019 , In-house algorithm (MATLAB R2017a) , Differential diagnosis (MCN vs. SCN) , 57 (31 MCNs, 26 SCNs) , SW , Radiologist , AP, PVP, DP , Radiomics model: AUC 0.989, Acc 94.7%, Sen 93.6%, Spe 96.2% Combined model (radiomics + radiological features): AUC 0.994, Acc 98.2%, Sen 96.8%, Spe 100%.

    Techniques: Biomarker Discovery, Histopathology, Software, Methylated DNA Immunoprecipitation