Review



convnet model  (Kaggle Inc)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    Kaggle Inc convnet model
    Balanced accuracy (%), precision (%), recall (%), and F 1 score of HIV-diagnosis prediction.
    Convnet Model, 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
    https://www.bioz.com/result/convnet model/product/Kaggle Inc
    Average 90 stars, based on 1 article reviews
    convnet model - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Training confounder-free deep learning models for medical applications"

    Article Title: Training confounder-free deep learning models for medical applications

    Journal: Nature Communications

    doi: 10.1038/s41467-020-19784-9

    Balanced accuracy (%), precision (%), recall (%), and F 1 score of HIV-diagnosis prediction.
    Figure Legend Snippet: Balanced accuracy (%), precision (%), recall (%), and F 1 score of HIV-diagnosis prediction.

    Techniques Used:

    a Age discrepancy ( p = 0.0002, two-tailed two-sample t -test) between n = 223 control (Ctrl) subjects and n = 122 HIV patients resulted in the baseline ConvNet learning the confounding effects ( b , d , f ), which were alleviated by the proposed CF-Net ( c , e , g ). Boxplots are characterized by minimum, first quartile, median, third quartile, and maximum. b , c HIV-prediction scores measured on a subset of n = 122 control and n = 122 HIV subjects with the same age distribution ( c -independent). d , e t-SNE visualization of the feature space learned by the deep-learning models. f , g Saliency maps corresponding to the voxel-level attention (larger attention means more discriminative voxels) by the models.
    Figure Legend Snippet: a Age discrepancy ( p = 0.0002, two-tailed two-sample t -test) between n = 223 control (Ctrl) subjects and n = 122 HIV patients resulted in the baseline ConvNet learning the confounding effects ( b , d , f ), which were alleviated by the proposed CF-Net ( c , e , g ). Boxplots are characterized by minimum, first quartile, median, third quartile, and maximum. b , c HIV-prediction scores measured on a subset of n = 122 control and n = 122 HIV subjects with the same age distribution ( c -independent). d , e t-SNE visualization of the feature space learned by the deep-learning models. f , g Saliency maps corresponding to the voxel-level attention (larger attention means more discriminative voxels) by the models.

    Techniques Used: Two Tailed Test, Control

    BAcc (precision and recall) on predicting sex from MRIs of NCANDA matched with respect to PDS. Optimal results were achieved when conditioning CF-Net on boys.
    Figure Legend Snippet: BAcc (precision and recall) on predicting sex from MRIs of NCANDA matched with respect to PDS. Optimal results were achieved when conditioning CF-Net on boys.

    Techniques Used:

    a Difference in the age distribution between n = 6, 833 boys and n = 5, 778 girls of the RSNA bone-age dataset ( p < 0.0001, two-tailed two-sample t -test). b Ground truth vs. predicted age of the ConvNet. ConvNet tended to predict higher age for girls than boys, indicating a confounding effect of sex. c This prediction gap between boys and girls was more pronounced in the age range of 110–200 months, but was significantly reduced by CF-Net, which modeled the dependency between F and c on a y -conditioned cohort. d Absolute prediction error (in months) of n = 3, 153 testing subjects produced by ConvNet and CF-Net with (or without) conditioning. Boxplots are characterized by minimum, first quartile, median, third quartile, and maximum. CF-Net with conditioning resulted in the most accurate prediction ( p < 0.0001, two-tailed two-sample t -test).
    Figure Legend Snippet: a Difference in the age distribution between n = 6, 833 boys and n = 5, 778 girls of the RSNA bone-age dataset ( p < 0.0001, two-tailed two-sample t -test). b Ground truth vs. predicted age of the ConvNet. ConvNet tended to predict higher age for girls than boys, indicating a confounding effect of sex. c This prediction gap between boys and girls was more pronounced in the age range of 110–200 months, but was significantly reduced by CF-Net, which modeled the dependency between F and c on a y -conditioned cohort. d Absolute prediction error (in months) of n = 3, 153 testing subjects produced by ConvNet and CF-Net with (or without) conditioning. Boxplots are characterized by minimum, first quartile, median, third quartile, and maximum. CF-Net with conditioning resulted in the most accurate prediction ( p < 0.0001, two-tailed two-sample t -test).

    Techniques Used: Two Tailed Test, Produced



    Similar Products

    convolutional neural network convnet model  (RenderX Inc)
    90
    RenderX Inc convolutional neural network convnet model
    Convolutional Neural Network Convnet Model, supplied by RenderX 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/convolutional neural network convnet model/product/RenderX Inc
    Average 90 stars, based on 1 article reviews
    convolutional neural network convnet model - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier
    deep learning model based on 1d convnets and bidirectional gru  (IEEE Access)
    90
    IEEE Access deep learning model based on 1d convnets and bidirectional gru
    Deep Learning Model Based On 1d Convnets And Bidirectional Gru, 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
    https://www.bioz.com/result/deep learning model based on 1d convnets and bidirectional gru/product/IEEE Access
    Average 90 stars, based on 1 article reviews
    deep learning model based on 1d convnets and bidirectional gru - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier
    convnet model  (Kaggle Inc)
    90
    Kaggle Inc convnet model
    Balanced accuracy (%), precision (%), recall (%), and F 1 score of HIV-diagnosis prediction.
    Convnet Model, 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
    https://www.bioz.com/result/convnet model/product/Kaggle Inc
    Average 90 stars, based on 1 article reviews
    convnet model - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Balanced accuracy (%), precision (%), recall (%), and F 1 score of HIV-diagnosis prediction.

    Journal: Nature Communications

    Article Title: Training confounder-free deep learning models for medical applications

    doi: 10.1038/s41467-020-19784-9

    Figure Lengend Snippet: Balanced accuracy (%), precision (%), recall (%), and F 1 score of HIV-diagnosis prediction.

    Article Snippet: We experimented on the 12,611 training images with ground-truth bone age (127.3 ± 41.2) and the ConvNet model publicly released on the Kaggle challenge page .

    Techniques:

    a Age discrepancy ( p = 0.0002, two-tailed two-sample t -test) between n = 223 control (Ctrl) subjects and n = 122 HIV patients resulted in the baseline ConvNet learning the confounding effects ( b , d , f ), which were alleviated by the proposed CF-Net ( c , e , g ). Boxplots are characterized by minimum, first quartile, median, third quartile, and maximum. b , c HIV-prediction scores measured on a subset of n = 122 control and n = 122 HIV subjects with the same age distribution ( c -independent). d , e t-SNE visualization of the feature space learned by the deep-learning models. f , g Saliency maps corresponding to the voxel-level attention (larger attention means more discriminative voxels) by the models.

    Journal: Nature Communications

    Article Title: Training confounder-free deep learning models for medical applications

    doi: 10.1038/s41467-020-19784-9

    Figure Lengend Snippet: a Age discrepancy ( p = 0.0002, two-tailed two-sample t -test) between n = 223 control (Ctrl) subjects and n = 122 HIV patients resulted in the baseline ConvNet learning the confounding effects ( b , d , f ), which were alleviated by the proposed CF-Net ( c , e , g ). Boxplots are characterized by minimum, first quartile, median, third quartile, and maximum. b , c HIV-prediction scores measured on a subset of n = 122 control and n = 122 HIV subjects with the same age distribution ( c -independent). d , e t-SNE visualization of the feature space learned by the deep-learning models. f , g Saliency maps corresponding to the voxel-level attention (larger attention means more discriminative voxels) by the models.

    Article Snippet: We experimented on the 12,611 training images with ground-truth bone age (127.3 ± 41.2) and the ConvNet model publicly released on the Kaggle challenge page .

    Techniques: Two Tailed Test, Control

    BAcc (precision and recall) on predicting sex from MRIs of NCANDA matched with respect to PDS. Optimal results were achieved when conditioning CF-Net on boys.

    Journal: Nature Communications

    Article Title: Training confounder-free deep learning models for medical applications

    doi: 10.1038/s41467-020-19784-9

    Figure Lengend Snippet: BAcc (precision and recall) on predicting sex from MRIs of NCANDA matched with respect to PDS. Optimal results were achieved when conditioning CF-Net on boys.

    Article Snippet: We experimented on the 12,611 training images with ground-truth bone age (127.3 ± 41.2) and the ConvNet model publicly released on the Kaggle challenge page .

    Techniques:

    a Difference in the age distribution between n = 6, 833 boys and n = 5, 778 girls of the RSNA bone-age dataset ( p < 0.0001, two-tailed two-sample t -test). b Ground truth vs. predicted age of the ConvNet. ConvNet tended to predict higher age for girls than boys, indicating a confounding effect of sex. c This prediction gap between boys and girls was more pronounced in the age range of 110–200 months, but was significantly reduced by CF-Net, which modeled the dependency between F and c on a y -conditioned cohort. d Absolute prediction error (in months) of n = 3, 153 testing subjects produced by ConvNet and CF-Net with (or without) conditioning. Boxplots are characterized by minimum, first quartile, median, third quartile, and maximum. CF-Net with conditioning resulted in the most accurate prediction ( p < 0.0001, two-tailed two-sample t -test).

    Journal: Nature Communications

    Article Title: Training confounder-free deep learning models for medical applications

    doi: 10.1038/s41467-020-19784-9

    Figure Lengend Snippet: a Difference in the age distribution between n = 6, 833 boys and n = 5, 778 girls of the RSNA bone-age dataset ( p < 0.0001, two-tailed two-sample t -test). b Ground truth vs. predicted age of the ConvNet. ConvNet tended to predict higher age for girls than boys, indicating a confounding effect of sex. c This prediction gap between boys and girls was more pronounced in the age range of 110–200 months, but was significantly reduced by CF-Net, which modeled the dependency between F and c on a y -conditioned cohort. d Absolute prediction error (in months) of n = 3, 153 testing subjects produced by ConvNet and CF-Net with (or without) conditioning. Boxplots are characterized by minimum, first quartile, median, third quartile, and maximum. CF-Net with conditioning resulted in the most accurate prediction ( p < 0.0001, two-tailed two-sample t -test).

    Article Snippet: We experimented on the 12,611 training images with ground-truth bone age (127.3 ± 41.2) and the ConvNet model publicly released on the Kaggle challenge page .

    Techniques: Two Tailed Test, Produced