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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
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Kaggle Inc
convnet model  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,
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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
Techniques:
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
Techniques: Two Tailed Test, Control
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
Techniques:
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
Techniques: Two Tailed Test, Produced