nasnet-large Search Results


xception  (MathWorks Inc)
90
MathWorks Inc xception
Xception, 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/xception/product/MathWorks Inc
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xception - by Bioz Stars, 2026-04
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nasnetlarge  (EyePACS LLC)
90
EyePACS LLC nasnetlarge
Nasnetlarge, supplied by EyePACS LLC, 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/nasnetlarge/product/EyePACS LLC
Average 90 stars, based on 1 article reviews
nasnetlarge - by Bioz Stars, 2026-04
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darknet19  (MathWorks Inc)
90
MathWorks Inc darknet19
Optimal predictive performance of InceptionResNetv2- and <t>NASNetLarge-based</t> models for predicting GTVp and GTVn regression, respectively. ( a ) Heatmap of the AUCs yielded by 25 InceptionResNetv2-based models (all combinations of five machine learning algorithms in rows and five feature selection algorithms in columns) predicting GTVp regression. ( b ) Corresponding heatmap of AUCs for the 25 NASNetLarge-based models predicting GTVn regression.
Darknet19, 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/darknet19/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
darknet19 - by Bioz Stars, 2026-04
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Image Search Results


Optimal predictive performance of InceptionResNetv2- and NASNetLarge-based models for predicting GTVp and GTVn regression, respectively. ( a ) Heatmap of the AUCs yielded by 25 InceptionResNetv2-based models (all combinations of five machine learning algorithms in rows and five feature selection algorithms in columns) predicting GTVp regression. ( b ) Corresponding heatmap of AUCs for the 25 NASNetLarge-based models predicting GTVn regression.

Journal: Scientific Reports

Article Title: A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy

doi: 10.1038/s41598-022-12170-z

Figure Lengend Snippet: Optimal predictive performance of InceptionResNetv2- and NASNetLarge-based models for predicting GTVp and GTVn regression, respectively. ( a ) Heatmap of the AUCs yielded by 25 InceptionResNetv2-based models (all combinations of five machine learning algorithms in rows and five feature selection algorithms in columns) predicting GTVp regression. ( b ) Corresponding heatmap of AUCs for the 25 NASNetLarge-based models predicting GTVn regression.

Article Snippet: We used the deep learning toolbox of MATLAB and downloaded 16 CNNs, SqueezeNet, GoogleNet, Inceptionv3, DenseNet201, MobileNetv2, ResNet18, ResNet50, ResNet101, Xception, InceptionResNetv2, ShuffleNet, NASNetMobile, NASNetLarge, DarkNet19, DarkNet53, and AlexNet, all pretrained on the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) .

Techniques: Selection

Mean 0.632 + bootstrap AUCs, sensitivity, and specificity of the deep learning-based radiomics, handcrafted radiomics features, clinical factors, and combined models for predicting nodal gross tumor volume (GTVn) regression.

Journal: Scientific Reports

Article Title: A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy

doi: 10.1038/s41598-022-12170-z

Figure Lengend Snippet: Mean 0.632 + bootstrap AUCs, sensitivity, and specificity of the deep learning-based radiomics, handcrafted radiomics features, clinical factors, and combined models for predicting nodal gross tumor volume (GTVn) regression.

Article Snippet: We used the deep learning toolbox of MATLAB and downloaded 16 CNNs, SqueezeNet, GoogleNet, Inceptionv3, DenseNet201, MobileNetv2, ResNet18, ResNet50, ResNet101, Xception, InceptionResNetv2, ShuffleNet, NASNetMobile, NASNetLarge, DarkNet19, DarkNet53, and AlexNet, all pretrained on the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) .

Techniques:

Activation maps of the initial CT images reveal salient features used by NASNetLarge-based models for prediction of GTVn regression. ( a ) Activation map of the initial CT image from a patient with large GTVn regression using NASNetLarge, the CNN yielding the highest prediction accuracy. The boost CT images are shown to illustrate the degree of regression. ( b ) Activation maps for all patients using NASNetLarge.

Journal: Scientific Reports

Article Title: A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy

doi: 10.1038/s41598-022-12170-z

Figure Lengend Snippet: Activation maps of the initial CT images reveal salient features used by NASNetLarge-based models for prediction of GTVn regression. ( a ) Activation map of the initial CT image from a patient with large GTVn regression using NASNetLarge, the CNN yielding the highest prediction accuracy. The boost CT images are shown to illustrate the degree of regression. ( b ) Activation maps for all patients using NASNetLarge.

Article Snippet: We used the deep learning toolbox of MATLAB and downloaded 16 CNNs, SqueezeNet, GoogleNet, Inceptionv3, DenseNet201, MobileNetv2, ResNet18, ResNet50, ResNet101, Xception, InceptionResNetv2, ShuffleNet, NASNetMobile, NASNetLarge, DarkNet19, DarkNet53, and AlexNet, all pretrained on the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) .

Techniques: Activation Assay