in-house program built in matlab 2019 Search Results


in-house scripts  (MathWorks Inc)
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MathWorks Inc in-house scripts
In House Scripts, 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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software matlab 2019  (MathWorks Inc)
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MathWorks Inc software matlab 2019
Software Matlab 2019, 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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matrix tools  (MathWorks Inc)
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MathWorks Inc matrix tools
Matrix Tools, 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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software developed in-house using matlab 2019  (MathWorks Inc)
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MathWorks Inc software developed in-house using matlab 2019
Software Developed In House Using Matlab 2019, 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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software developed in-house using matlab 2019 - by Bioz Stars, 2026-04
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in-house algorithm matlab r2016a  (MathWorks Inc)
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MathWorks Inc in-house algorithm matlab r2016a
Applications of radiomics in pancreatic CT images.
In House Algorithm Matlab R2016a, 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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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
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in-house matlab v.2019 toolbox  (MathWorks Inc)
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MathWorks Inc in-house matlab v.2019 toolbox
Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
In House Matlab V.2019 Toolbox, 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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in-house matlab 2019 program  (MathWorks Inc)
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MathWorks Inc in-house matlab 2019 program
Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
In House Matlab 2019 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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matlab version 9.7.0.1471314  (MathWorks Inc)
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MathWorks Inc matlab version 9.7.0.1471314
Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
Matlab Version 9.7.0.1471314, 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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in-house matlab code  (MathWorks Inc)
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MathWorks Inc in-house matlab code
Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
In House Matlab Code, 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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in-house (matlab)  (MathWorks Inc)
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MathWorks Inc in-house (matlab)
Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house <t>MATLAB</t> code was used to overlay the visualizations.
In House (Matlab), 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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in-house matlab 2015b  (MathWorks Inc)
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MathWorks Inc in-house matlab 2015b
Summary of general study characteristics
In House Matlab 2015b, 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


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: Liang , 2019 , In-house algorithm (MATLAB R2016a) , pNET grading (G1 vs. G2/G3) , 137 (70 grade 1, 67 grade 2/3) , RW (86 TS, 51 VS) , Histopathology , AP , Nomogram (eight radiomics features + clinical stage): AUC 0.891.

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: Liang , 2019 , In-house algorithm (MATLAB R2016a) , pNET grading (G1 vs. G2/G3) , 137 (70 grade 1, 67 grade 2/3) , RW (86 TS, 51 VS) , Histopathology , AP , Nomogram (eight radiomics features + clinical stage): AUC 0.891.

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

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

Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house MATLAB code was used to overlay the visualizations.

Journal: Heliyon

Article Title: An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings

doi: 10.1016/j.heliyon.2024.e29602

Figure Lengend Snippet: Visualizations of one of the topmost features of M R (orginal_collage2D_glcmV_JointEnergyEntorpy) (R1-R4) and M P (Shape: 5 %/95 % invariant 1) (P1–P4) between four different patients. The columns 1,2 represent patients with a low risk of rising PSA and columns 3,4 represent patients with a high risk of rising PSA. It can be observed that the visualizations of Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) gray level cooccurrence matrix (GLCM) radiomic feature on apparent diffusion coefficient (ADC) maps indicates the presence of higher density of high entropy regions for which M R has classified as rPSA + ( : R3, R4), as compared to the ones for which M R has classified as rPSA − ( : R1, R2). Similarly, the pathomic visualizations of Shape: 5 %/95 % invariant 1 depicts that a high risk of rising PSA with more aggressive cancer leads to uniformly small, malformed lumen resulting in a lower 5th/95th percentile ratio (lower range) ( : P3, P4) as compared to cases with lower risk of rising PSA ( : P1, P2). For radiomic visualizations, the feature array output from the pyradiomics package was used to overlay on top of the ADC using matplotlib package. For pathomics visualizations, in-house MATLAB code was used to overlay the visualizations.

Article Snippet: The pathomic feature extraction was performed using an in-house MATLAB V.2019 (MathWorks, Natick, Massachusetts, USA) toolbox.

Techniques: Diffusion-based Assay

Summary of general study characteristics

Journal: European Journal of Nuclear Medicine and Molecular Imaging

Article Title: Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy

doi: 10.1007/s00259-021-05658-9

Figure Lengend Snippet: Summary of general study characteristics

Article Snippet: Xie: 2019 [ ] , ESCC Train 2012–2016 Val. 2008–2011 , CT , Inst 1: 120 kVp, 406 mAs, 3–5 mm Inst 2: 120 kVp, 150 mAs, 3–8 mm Voxel size: 1 × 1 × 5 mm3 , dCCRT , 87 (train) 46 (val.) , HF , In-house (Matlab 2015b) , No.

Techniques: Imaging, Software