home-built graphic user interface (gui) based matlab program Search Results


home-built matlab graphical user interface  (MathWorks Inc)
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home-built graphic user interface (gui) based matlab program  (MathWorks Inc)
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MathWorks Inc home-built graphic user interface (gui) based matlab program
Home Built Graphic User Interface (Gui) Based Matlab Program, 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
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mr radiomics platform  (MathWorks Inc)
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Mr Radiomics Platform, 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
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mr radiomics platform (mrp) graphic interface built in  (MathWorks Inc)
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<t>Radiomics,</t> radiogenomics, and deep learning workflow
Mr Radiomics Platform (Mrp) Graphic Interface Built In, 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
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Image Search Results


Radiomics, radiogenomics, and deep learning workflow

Journal: Cancer Imaging

Article Title: -New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates

doi: 10.1186/s40644-024-00769-6

Figure Lengend Snippet: Radiomics, radiogenomics, and deep learning workflow

Article Snippet: Lu et al. 2018 [ ] , 214 (with independent validation on a set of 70 patients) , T1W, CET1W, T2W, FLAIR, DWI , Semi-automatic with Manual correction , Home-made software, MR Radiomics Platform (MRP) Graphic interface built in Matlab , A maximum of 39,212 MR radiomic features generated for each subject. , Texture measurements describing spatial variations of tumor intensity found to be the most illustrative for the IDH and 1p/19q genotypes , , , , Radiomics, Machine learning , An AUC of 0.922 achieved on the training dataset while and Accuracy of 80% yielded in predicting the 1p/19q co deletion status..

Techniques:

Diagnostic markers

Journal: Cancer Imaging

Article Title: -New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates

doi: 10.1186/s40644-024-00769-6

Figure Lengend Snippet: Diagnostic markers

Article Snippet: Lu et al. 2018 [ ] , 214 (with independent validation on a set of 70 patients) , T1W, CET1W, T2W, FLAIR, DWI , Semi-automatic with Manual correction , Home-made software, MR Radiomics Platform (MRP) Graphic interface built in Matlab , A maximum of 39,212 MR radiomic features generated for each subject. , Texture measurements describing spatial variations of tumor intensity found to be the most illustrative for the IDH and 1p/19q genotypes , , , , Radiomics, Machine learning , An AUC of 0.922 achieved on the training dataset while and Accuracy of 80% yielded in predicting the 1p/19q co deletion status..

Techniques: Diagnostic Assay, Sequencing, Software, Mutagenesis, Generated, Biomarker Discovery, Imaging, Diffusion-based Assay, Construct, Amplification, Selection, Plasmid Preparation, Derivative Assay

Prognostic markers

Journal: Cancer Imaging

Article Title: -New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates

doi: 10.1186/s40644-024-00769-6

Figure Lengend Snippet: Prognostic markers

Article Snippet: Lu et al. 2018 [ ] , 214 (with independent validation on a set of 70 patients) , T1W, CET1W, T2W, FLAIR, DWI , Semi-automatic with Manual correction , Home-made software, MR Radiomics Platform (MRP) Graphic interface built in Matlab , A maximum of 39,212 MR radiomic features generated for each subject. , Texture measurements describing spatial variations of tumor intensity found to be the most illustrative for the IDH and 1p/19q genotypes , , , , Radiomics, Machine learning , An AUC of 0.922 achieved on the training dataset while and Accuracy of 80% yielded in predicting the 1p/19q co deletion status..

Techniques: Sequencing, Software, Methylation, Imaging, Extraction, Diffusion-based Assay, Biomarker Discovery, Construct, Selection