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Philips Healthcare
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SafeGraph Inc
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Decon Laboratories
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PhaseSpace Inc
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Texas Instruments
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Image Search Results
Journal: Frontiers in Computational Neuroscience
Article Title: U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis
doi: 10.3389/fncom.2024.1387004
Figure Lengend Snippet: The reconstruction performance using different models.
Article Snippet: Measuring the similarity between generated and empirical time-series data is a crucial step in evaluating the performance of the proposed model. Three metrics can be used for this purpose, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination of the prediction (R2) (Ma et al., ), and
Techniques:
Journal: Frontiers in Computational Neuroscience
Article Title: U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis
doi: 10.3389/fncom.2024.1387004
Figure Lengend Snippet: The reconstruction performance using different models for different noise levels.
Article Snippet: Measuring the similarity between generated and empirical time-series data is a crucial step in evaluating the performance of the proposed model. Three metrics can be used for this purpose, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination of the prediction (R2) (Ma et al., ), and
Techniques:
Journal: Frontiers in Computational Neuroscience
Article Title: U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis
doi: 10.3389/fncom.2024.1387004
Figure Lengend Snippet: Influence of different model's module on the reconstruction performance.
Article Snippet: Measuring the similarity between generated and empirical time-series data is a crucial step in evaluating the performance of the proposed model. Three metrics can be used for this purpose, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination of the prediction (R2) (Ma et al., ), and
Techniques: