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self-label convnet  (MathWorks Inc)


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    MathWorks Inc self-label convnet
    a. Bench-top microscope model [info] used for capturing cell images in commonly-used, thick bottomed, plastic cell culture flasks, b. Example brightfield images of both multi-cell regions (1216×1616 pixels) and manually cropped single cell regions (224×224 pixels). Note the camera used was not monochromatic and the images were converted to grayscale to reduce dimensionality and improve training efficiency, c. Quantile normalization of the image intensity distribution was performed for each cell to a reference distribution constructed from an arbitrarily selected single cell image, d. Cartoon of the proposed <t>ConvNet</t> architecture. Six quadruplets of convolutional, ReLU, Batch Normalization and average pooling layers were constructed. A single fully connected layer was constructed before the Softmax and classification layers, e. Illustration of Cell Type Classification from low-res flask images through artificial neural network. Labelled cells (yellow, blue) in labelled flasks should be fed into the neural network as training data, and unlabelled flasks shoule be treated as validation data. The trained ConvNet model is able to predict cell type with low error given novel cells with unknown label.
    Self Label Convnet, 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/result/self-label convnet/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    self-label convnet - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Cell type classification and unsupervised morphological phenotype identification from low-res images with deep learning"

    Article Title: Cell type classification and unsupervised morphological phenotype identification from low-res images with deep learning

    Journal: bioRxiv

    doi: 10.1101/533216

    a. Bench-top microscope model [info] used for capturing cell images in commonly-used, thick bottomed, plastic cell culture flasks, b. Example brightfield images of both multi-cell regions (1216×1616 pixels) and manually cropped single cell regions (224×224 pixels). Note the camera used was not monochromatic and the images were converted to grayscale to reduce dimensionality and improve training efficiency, c. Quantile normalization of the image intensity distribution was performed for each cell to a reference distribution constructed from an arbitrarily selected single cell image, d. Cartoon of the proposed ConvNet architecture. Six quadruplets of convolutional, ReLU, Batch Normalization and average pooling layers were constructed. A single fully connected layer was constructed before the Softmax and classification layers, e. Illustration of Cell Type Classification from low-res flask images through artificial neural network. Labelled cells (yellow, blue) in labelled flasks should be fed into the neural network as training data, and unlabelled flasks shoule be treated as validation data. The trained ConvNet model is able to predict cell type with low error given novel cells with unknown label.
    Figure Legend Snippet: a. Bench-top microscope model [info] used for capturing cell images in commonly-used, thick bottomed, plastic cell culture flasks, b. Example brightfield images of both multi-cell regions (1216×1616 pixels) and manually cropped single cell regions (224×224 pixels). Note the camera used was not monochromatic and the images were converted to grayscale to reduce dimensionality and improve training efficiency, c. Quantile normalization of the image intensity distribution was performed for each cell to a reference distribution constructed from an arbitrarily selected single cell image, d. Cartoon of the proposed ConvNet architecture. Six quadruplets of convolutional, ReLU, Batch Normalization and average pooling layers were constructed. A single fully connected layer was constructed before the Softmax and classification layers, e. Illustration of Cell Type Classification from low-res flask images through artificial neural network. Labelled cells (yellow, blue) in labelled flasks should be fed into the neural network as training data, and unlabelled flasks shoule be treated as validation data. The trained ConvNet model is able to predict cell type with low error given novel cells with unknown label.

    Techniques Used: Microscopy, Cell Culture, Construct

    a. Self-Label ConvNet Architecture Illustration. The group of augemented copies for each cell are considered unique classes, yielding the same number of classes in the final layer as there are cells used to train the network. The [l]ast [c]onvolutional [a]ctivation or ‘LCA’ feature space, labeled in green, is the structure of interest for the following mophological phenotype clustering, b. Training profile of Self-Label ConvNet. An accuracy of nearly 100% can be achieved for both training data and validation data, and a Softmax loss of nearly 0 can be achieved for both training data and validatioan data, c. Workflow for acquiring the LCA feature space an example cell. Novel cells are input into the pre-trained Self-Label ConvNet and the activations of the last convolutional layer are recorded as 32 3×3 matrices for each cell input. The matrices are then flattened to a vector of length 288, each element representing one ‘feature’ of the input cell, d. LCA matrix: LCA feature maps for many cells across all densities (2208 cells total) are displayed as rows in a matrix (size 2208×288) with each column representing one feature in the LCA, e. Clustering outcome for the LCA matrix applying k -means to rows according to Euclidean distance with k = 11. Clusters are shown after reshuffling the cell indices based on their cluster index., f. Cross-density cluster comparison. Two flasks of two densities are shown. For each flask, the fraction of cells belonging to each of the k = 11 clusters are displayed. Clusters with significantly different representations between densities are colored, g. Morphological Analysis: Two clusters of cells dominated by low density (cluster #10) and high density (cluster #3) respectively, were analyzed. Morphological properties for cells within these clusters were calculated with CellProfiler, and two features (SkeletonEndpoints and TextureSumVariance5) were chosen to generate a 2D projection, illustrating clear distinguishability in a low dimensional morphological feature space. High density biased cluster (cluster #3) was labeled in red and low density biased cluster (cluster #10) was labeled in blue.
    Figure Legend Snippet: a. Self-Label ConvNet Architecture Illustration. The group of augemented copies for each cell are considered unique classes, yielding the same number of classes in the final layer as there are cells used to train the network. The [l]ast [c]onvolutional [a]ctivation or ‘LCA’ feature space, labeled in green, is the structure of interest for the following mophological phenotype clustering, b. Training profile of Self-Label ConvNet. An accuracy of nearly 100% can be achieved for both training data and validation data, and a Softmax loss of nearly 0 can be achieved for both training data and validatioan data, c. Workflow for acquiring the LCA feature space an example cell. Novel cells are input into the pre-trained Self-Label ConvNet and the activations of the last convolutional layer are recorded as 32 3×3 matrices for each cell input. The matrices are then flattened to a vector of length 288, each element representing one ‘feature’ of the input cell, d. LCA matrix: LCA feature maps for many cells across all densities (2208 cells total) are displayed as rows in a matrix (size 2208×288) with each column representing one feature in the LCA, e. Clustering outcome for the LCA matrix applying k -means to rows according to Euclidean distance with k = 11. Clusters are shown after reshuffling the cell indices based on their cluster index., f. Cross-density cluster comparison. Two flasks of two densities are shown. For each flask, the fraction of cells belonging to each of the k = 11 clusters are displayed. Clusters with significantly different representations between densities are colored, g. Morphological Analysis: Two clusters of cells dominated by low density (cluster #10) and high density (cluster #3) respectively, were analyzed. Morphological properties for cells within these clusters were calculated with CellProfiler, and two features (SkeletonEndpoints and TextureSumVariance5) were chosen to generate a 2D projection, illustrating clear distinguishability in a low dimensional morphological feature space. High density biased cluster (cluster #3) was labeled in red and low density biased cluster (cluster #10) was labeled in blue.

    Techniques Used: Labeling, Plasmid Preparation



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    self-label convnet  (MathWorks Inc)
    90
    MathWorks Inc self-label convnet
    a. Bench-top microscope model [info] used for capturing cell images in commonly-used, thick bottomed, plastic cell culture flasks, b. Example brightfield images of both multi-cell regions (1216×1616 pixels) and manually cropped single cell regions (224×224 pixels). Note the camera used was not monochromatic and the images were converted to grayscale to reduce dimensionality and improve training efficiency, c. Quantile normalization of the image intensity distribution was performed for each cell to a reference distribution constructed from an arbitrarily selected single cell image, d. Cartoon of the proposed <t>ConvNet</t> architecture. Six quadruplets of convolutional, ReLU, Batch Normalization and average pooling layers were constructed. A single fully connected layer was constructed before the Softmax and classification layers, e. Illustration of Cell Type Classification from low-res flask images through artificial neural network. Labelled cells (yellow, blue) in labelled flasks should be fed into the neural network as training data, and unlabelled flasks shoule be treated as validation data. The trained ConvNet model is able to predict cell type with low error given novel cells with unknown label.
    Self Label Convnet, 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/result/self-label convnet/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    self-label convnet - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    a. Bench-top microscope model [info] used for capturing cell images in commonly-used, thick bottomed, plastic cell culture flasks, b. Example brightfield images of both multi-cell regions (1216×1616 pixels) and manually cropped single cell regions (224×224 pixels). Note the camera used was not monochromatic and the images were converted to grayscale to reduce dimensionality and improve training efficiency, c. Quantile normalization of the image intensity distribution was performed for each cell to a reference distribution constructed from an arbitrarily selected single cell image, d. Cartoon of the proposed ConvNet architecture. Six quadruplets of convolutional, ReLU, Batch Normalization and average pooling layers were constructed. A single fully connected layer was constructed before the Softmax and classification layers, e. Illustration of Cell Type Classification from low-res flask images through artificial neural network. Labelled cells (yellow, blue) in labelled flasks should be fed into the neural network as training data, and unlabelled flasks shoule be treated as validation data. The trained ConvNet model is able to predict cell type with low error given novel cells with unknown label.

    Journal: bioRxiv

    Article Title: Cell type classification and unsupervised morphological phenotype identification from low-res images with deep learning

    doi: 10.1101/533216

    Figure Lengend Snippet: a. Bench-top microscope model [info] used for capturing cell images in commonly-used, thick bottomed, plastic cell culture flasks, b. Example brightfield images of both multi-cell regions (1216×1616 pixels) and manually cropped single cell regions (224×224 pixels). Note the camera used was not monochromatic and the images were converted to grayscale to reduce dimensionality and improve training efficiency, c. Quantile normalization of the image intensity distribution was performed for each cell to a reference distribution constructed from an arbitrarily selected single cell image, d. Cartoon of the proposed ConvNet architecture. Six quadruplets of convolutional, ReLU, Batch Normalization and average pooling layers were constructed. A single fully connected layer was constructed before the Softmax and classification layers, e. Illustration of Cell Type Classification from low-res flask images through artificial neural network. Labelled cells (yellow, blue) in labelled flasks should be fed into the neural network as training data, and unlabelled flasks shoule be treated as validation data. The trained ConvNet model is able to predict cell type with low error given novel cells with unknown label.

    Article Snippet: A graphical representation of the Self-Label ConvNet designed for cell morphological phenotype clustering within one cell type via MATLAB 2018a (MathWorks) was displayed in .

    Techniques: Microscopy, Cell Culture, Construct

    a. Self-Label ConvNet Architecture Illustration. The group of augemented copies for each cell are considered unique classes, yielding the same number of classes in the final layer as there are cells used to train the network. The [l]ast [c]onvolutional [a]ctivation or ‘LCA’ feature space, labeled in green, is the structure of interest for the following mophological phenotype clustering, b. Training profile of Self-Label ConvNet. An accuracy of nearly 100% can be achieved for both training data and validation data, and a Softmax loss of nearly 0 can be achieved for both training data and validatioan data, c. Workflow for acquiring the LCA feature space an example cell. Novel cells are input into the pre-trained Self-Label ConvNet and the activations of the last convolutional layer are recorded as 32 3×3 matrices for each cell input. The matrices are then flattened to a vector of length 288, each element representing one ‘feature’ of the input cell, d. LCA matrix: LCA feature maps for many cells across all densities (2208 cells total) are displayed as rows in a matrix (size 2208×288) with each column representing one feature in the LCA, e. Clustering outcome for the LCA matrix applying k -means to rows according to Euclidean distance with k = 11. Clusters are shown after reshuffling the cell indices based on their cluster index., f. Cross-density cluster comparison. Two flasks of two densities are shown. For each flask, the fraction of cells belonging to each of the k = 11 clusters are displayed. Clusters with significantly different representations between densities are colored, g. Morphological Analysis: Two clusters of cells dominated by low density (cluster #10) and high density (cluster #3) respectively, were analyzed. Morphological properties for cells within these clusters were calculated with CellProfiler, and two features (SkeletonEndpoints and TextureSumVariance5) were chosen to generate a 2D projection, illustrating clear distinguishability in a low dimensional morphological feature space. High density biased cluster (cluster #3) was labeled in red and low density biased cluster (cluster #10) was labeled in blue.

    Journal: bioRxiv

    Article Title: Cell type classification and unsupervised morphological phenotype identification from low-res images with deep learning

    doi: 10.1101/533216

    Figure Lengend Snippet: a. Self-Label ConvNet Architecture Illustration. The group of augemented copies for each cell are considered unique classes, yielding the same number of classes in the final layer as there are cells used to train the network. The [l]ast [c]onvolutional [a]ctivation or ‘LCA’ feature space, labeled in green, is the structure of interest for the following mophological phenotype clustering, b. Training profile of Self-Label ConvNet. An accuracy of nearly 100% can be achieved for both training data and validation data, and a Softmax loss of nearly 0 can be achieved for both training data and validatioan data, c. Workflow for acquiring the LCA feature space an example cell. Novel cells are input into the pre-trained Self-Label ConvNet and the activations of the last convolutional layer are recorded as 32 3×3 matrices for each cell input. The matrices are then flattened to a vector of length 288, each element representing one ‘feature’ of the input cell, d. LCA matrix: LCA feature maps for many cells across all densities (2208 cells total) are displayed as rows in a matrix (size 2208×288) with each column representing one feature in the LCA, e. Clustering outcome for the LCA matrix applying k -means to rows according to Euclidean distance with k = 11. Clusters are shown after reshuffling the cell indices based on their cluster index., f. Cross-density cluster comparison. Two flasks of two densities are shown. For each flask, the fraction of cells belonging to each of the k = 11 clusters are displayed. Clusters with significantly different representations between densities are colored, g. Morphological Analysis: Two clusters of cells dominated by low density (cluster #10) and high density (cluster #3) respectively, were analyzed. Morphological properties for cells within these clusters were calculated with CellProfiler, and two features (SkeletonEndpoints and TextureSumVariance5) were chosen to generate a 2D projection, illustrating clear distinguishability in a low dimensional morphological feature space. High density biased cluster (cluster #3) was labeled in red and low density biased cluster (cluster #10) was labeled in blue.

    Article Snippet: A graphical representation of the Self-Label ConvNet designed for cell morphological phenotype clustering within one cell type via MATLAB 2018a (MathWorks) was displayed in .

    Techniques: Labeling, Plasmid Preparation