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automated stroke prediction using machine learning  (IEEE Access)
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omics data integration and machine learning algorithm  (Omics Data Automation)
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efficient algorithms of machine learning and deep learning (dl) in multi-omics data analysis  (Omics Data Automation)
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a machine learning neuroimaging challenge for automated diagnosis of mild cognitive impairment  (Kaggle Inc)
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A Machine Learning Neuroimaging Challenge For Automated Diagnosis Of Mild Cognitive Impairment, supplied by Kaggle 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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automated machine learning product version 5b1d33  (DataRobot Inc)
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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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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
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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: