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Found 7 papers, 1 papers with code
Brain MRI-to-PET Synthesis using 3D Convolutional Attention Networks
Such methods may enable more widespread and accurate CBF evaluation in larger cohorts who cannot undergo PET imaging due to radiation concerns, lack of access, or logistic challenges.
Cascaded Deep Hybrid Models for Multistep Household Energy Consumption Forecasting
This study introduces two hybrid cascaded models for forecasting multistep household power consumption in different resolutions.
Multi-Channel Vision Transformer for Epileptic Seizure Prediction
Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of awareness.
Multi-task Deep Learning for Cerebrovascular Disease Classification and MRI-to-PET Translation
Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid stenosis, aneurysms, and stroke.
Household Energy Consumption Prediction Using the Stationary Wavelet Transform and Transformers
This study proposes a new technique using machine learning models based on the stationary wavelet transform (SWT) and transformers to forecast household power consumption in different resolutions.
Epileptic Seizure Prediction: A Semi-Dilated Convolutional Neural Network Architecture
Accurate prediction of epileptic seizures has remained elusive, despite the many advances in machine learning and time-series classification.
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction
2) The traditional PCA is not a reliable method for iEEG data reduction in seizure prediction.
