Journal: PLoS ONE

Article Title: Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

doi: 10.1371/journal.pone.0178410

Figure Lengend Snippet: Performance for all categories using the proposed method (ConvNet & LRBSF). Best and worst performance of individual participant is also mentioned.

Article Snippet: Because ConvNet is a complete framework, most of the studies have used the ConvNet classifier (softmax) for prediction/classification [ ].

Techniques: Selection

Journal: PLoS ONE

Article Title: Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

doi: 10.1371/journal.pone.0178410

Figure Lengend Snippet: Significant difference (p-value) of accuracies between the proposed and other methods.

Article Snippet: Because ConvNet is a complete framework, most of the studies have used the ConvNet classifier (softmax) for prediction/classification [ ].

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

Article Snippet: The ConvNet was based on the publicly released implementation by the Kaggle challenge .

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: The ConvNet was based on the publicly released implementation by the Kaggle challenge .

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: The ConvNet was based on the publicly released implementation by the Kaggle challenge .

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: The ConvNet was based on the publicly released implementation by the Kaggle challenge .

Techniques: Two Tailed Test, Produced

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:

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

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:

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

Journal: Journal of Imaging

Article Title: Retinal Disease Detection Using Deep Learning Techniques: A Comprehensive Review

doi: 10.3390/jimaging9040084

Figure Lengend Snippet: Summary of Deep Learning Methods for DR Classification.

Article Snippet: [ ] , ConvNet , EyePACS, e-optha, DiaretDB1 , , , , 0.954, 0.949, 0.955.

Techniques: