pretrained vgg19 Search Results


imagenet pretrained encoder backbones  (Siemens Healthineers)
86
Siemens Healthineers imagenet pretrained encoder backbones
Imagenet Pretrained Encoder Backbones, supplied by Siemens Healthineers, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/imagenet pretrained encoder backbones/product/Siemens Healthineers
Average 86 stars, based on 1 article reviews
imagenet pretrained encoder backbones - by Bioz Stars, 2026-05
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pretrained vgg19  (MathWorks Inc)
96
MathWorks Inc pretrained vgg19
Deep learning (DL)–based feature extraction scheme using <t>VGG19.</t> VGG19 contains 16 convolutional layers, 5 max-pooling layers, and 3 fully connected layers. The average-pooling layers were used for extracting DL-based features. Feature maps and feature vectors after every layer are shown as cuboids and rectangles, respectively. The feature map depth and feature number are shown. A concatenation of fluid-attenuated inversion recovery (FLAIR), T2-weighted (T2w), and contrast-enhanced T1-weighted (CE-T1w) regions of interest (ROIs) was input into the pretrained VGG19 for feature extraction. By average-pooling along the spatial dimensions, 1472 DL-based features were extracted from max-pooling feature maps. Abbreviations: Conv = convolutional layer; ReLU = rectified linear unit.
Pretrained Vgg19, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/pretrained vgg19/product/MathWorks Inc
Average 96 stars, based on 1 article reviews
pretrained vgg19 - by Bioz Stars, 2026-05
96/100 stars
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vgg19  (Great Basin Corp)
90
Great Basin Corp vgg19
Deep learning (DL)–based feature extraction scheme using <t>VGG19.</t> VGG19 contains 16 convolutional layers, 5 max-pooling layers, and 3 fully connected layers. The average-pooling layers were used for extracting DL-based features. Feature maps and feature vectors after every layer are shown as cuboids and rectangles, respectively. The feature map depth and feature number are shown. A concatenation of fluid-attenuated inversion recovery (FLAIR), T2-weighted (T2w), and contrast-enhanced T1-weighted (CE-T1w) regions of interest (ROIs) was input into the pretrained VGG19 for feature extraction. By average-pooling along the spatial dimensions, 1472 DL-based features were extracted from max-pooling feature maps. Abbreviations: Conv = convolutional layer; ReLU = rectified linear unit.
Vgg19, supplied by Great Basin Corp, 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/vgg19/product/Great Basin Corp
Average 90 stars, based on 1 article reviews
vgg19 - by Bioz Stars, 2026-05
90/100 stars
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Image Search Results


Deep learning (DL)–based feature extraction scheme using VGG19. VGG19 contains 16 convolutional layers, 5 max-pooling layers, and 3 fully connected layers. The average-pooling layers were used for extracting DL-based features. Feature maps and feature vectors after every layer are shown as cuboids and rectangles, respectively. The feature map depth and feature number are shown. A concatenation of fluid-attenuated inversion recovery (FLAIR), T2-weighted (T2w), and contrast-enhanced T1-weighted (CE-T1w) regions of interest (ROIs) was input into the pretrained VGG19 for feature extraction. By average-pooling along the spatial dimensions, 1472 DL-based features were extracted from max-pooling feature maps. Abbreviations: Conv = convolutional layer; ReLU = rectified linear unit.

Journal: Advances in Radiation Oncology

Article Title: An Automatic Deep Learning–Based Workflow for Glioblastoma Survival Prediction Using Preoperative Multimodal MR Images: A Feasibility Study

doi: 10.1016/j.adro.2021.100746

Figure Lengend Snippet: Deep learning (DL)–based feature extraction scheme using VGG19. VGG19 contains 16 convolutional layers, 5 max-pooling layers, and 3 fully connected layers. The average-pooling layers were used for extracting DL-based features. Feature maps and feature vectors after every layer are shown as cuboids and rectangles, respectively. The feature map depth and feature number are shown. A concatenation of fluid-attenuated inversion recovery (FLAIR), T2-weighted (T2w), and contrast-enhanced T1-weighted (CE-T1w) regions of interest (ROIs) was input into the pretrained VGG19 for feature extraction. By average-pooling along the spatial dimensions, 1472 DL-based features were extracted from max-pooling feature maps. Abbreviations: Conv = convolutional layer; ReLU = rectified linear unit.

Article Snippet: We used a pretrained VGG19 that is available in the deep learning toolbox (version 12.0) from MATLAB (version 9.5, R2018b).

Techniques: Extraction