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op230 automated real time endoscopic scoring based on machine learning in ulcerative colitis  (PENTAX Medical Company)
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PENTAX Medical Company op230 automated real time endoscopic scoring based on machine learning in ulcerative colitis
Op230 Automated Real Time Endoscopic Scoring Based On Machine Learning In Ulcerative Colitis, supplied by PENTAX Medical Company, 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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automated stroke prediction using machine learning  (IEEE Access)
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IEEE Access automated stroke prediction using machine learning
Automated Stroke Prediction Using Machine Learning, supplied by IEEE Access, 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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automated machine learning  (Ledell Inc)
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Ledell Inc automated machine learning
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automated machine learning product version 5b1d33  (DataRobot Inc)
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Automated Machine Learning Product Version 5b1d33, supplied by DataRobot 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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cloud-based automated machine learning platform  (BigML Inc)
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automated, machine learning-derived algorithms  (Ultromics Ltd)
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automated, machine learning algorithms  (Ultromics Ltd)
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Ultromics Ltd automated, machine learning algorithms
Automated, Machine Learning Algorithms, supplied by Ultromics Ltd, 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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patternquant semi-automated machine learning algorithm  (3DHistech ltd)
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3DHistech ltd patternquant semi-automated machine learning algorithm
Automated image analysis using the <t>PatternQuant</t> machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]
Patternquant Semi Automated Machine Learning Algorithm, supplied by 3DHistech ltd, 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/patternquant semi-automated machine learning algorithm/product/3DHistech ltd
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machine learning for all areas of automation  (Beckhoff Automation GmbH Co KG)
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Beckhoff Automation GmbH Co KG machine learning for all areas of automation
Automated image analysis using the <t>PatternQuant</t> machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]
Machine Learning For All Areas Of Automation, supplied by Beckhoff Automation GmbH Co KG, 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/machine learning for all areas of automation/product/Beckhoff Automation GmbH Co KG
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machine learning for all areas of automation - by Bioz Stars, 2026-05
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automated machine learning  (ARKRAY Inc)
90
ARKRAY Inc automated machine learning
Automated image analysis using the <t>PatternQuant</t> machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]
Automated Machine Learning, supplied by ARKRAY 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/automated machine learning/product/ARKRAY Inc
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disease agnostic gene prioritisation with automated machine learning  (AstraZeneca ltd)
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AstraZeneca ltd disease agnostic gene prioritisation with automated machine learning
Automated image analysis using the <t>PatternQuant</t> machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]
Disease Agnostic Gene Prioritisation With Automated Machine Learning, supplied by AstraZeneca ltd, 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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fully automated machine learning with interpretable analysis of results framework  (SAS institute)
90
SAS institute fully automated machine learning with interpretable analysis of results framework
Automated image analysis using the <t>PatternQuant</t> machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]
Fully Automated Machine Learning With Interpretable Analysis Of Results Framework, supplied by SAS institute, 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


Automated image analysis using the PatternQuant machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]

Journal: Histopathology

Article Title: Myelofibrosis progression grading based on type I and type III collagen and fibrillin 1 expression boosted by whole slide image analysis

doi: 10.1111/his.14846

Figure Lengend Snippet: Automated image analysis using the PatternQuant machine learning algorithm for measuring the proportions of fibrillin 1 immunoperoxidase reactions ( A,C, brown) within representative annotated areas of myelofibrotic bone marrow sections. Image segmentation reveals areas occupied by the immunoreactions (red), the immune‐negative tissue (green) and the cell‐free regions (yellow) ( B,D ). Higher power confirms the accuracy of segmentation ( B,D ). Highlighted numbers in ( D ) show measured areas in μm 2 and in % proportions. Graphs show the statistical correlations between fibrillin 1 quantitative results and Gomori's silver grades using both the Kruskal–Wallis test and Wilcoxon's post‐hoc test ( E ). The seven MF cases which were up‐scaled (red triangles) and six MF cases which were down‐scaled (green triangles) at visual scoring segregated to the upper and lower regions, respectively, within their MF categories in the box‐plots. The overlapping distribution curves of quantitative results within MF‐grades ( F ) confirms the continuous nature of myelofibrosis progression. [Color figure can be viewed at wileyonlinelibrary.com ]

Article Snippet: The image analysis of fibrillin 1 immunoreactions was performed using the PatternQuant semi‐automated machine learning algorithm of the QuantCenter software package (all 3DHistech).

Techniques: