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Found 9 papers, 2 papers with code
NeuroMorphix: A Novel Brain MRI Asymmetry-specific Feature Construction Approach For Seizure Recurrence Prediction
State-of-the-art classification algorithms were trained and tested using NeuroMorphix features to predict seizure recurrence.
Machine Learning Applications in Traumatic Brain Injury: A Spotlight on Mild TBI
As such, we review the state-of-the-art Machine Learning (ML) techniques applied to clinical information and CT scans in TBI, with a particular focus on mTBI.
Enhancing mTBI Diagnosis with Residual Triplet Convolutional Neural Network Using 3D CT
To address these challenges, we propose a Residual Triplet Convolutional Neural Network (RTCNN) model to distinguish between mTBI cases and healthy ones by embedding 3D CT scans into a feature space.
Interpretable 3D Multi-Modal Residual Convolutional Neural Network for Mild Traumatic Brain Injury Diagnosis
Notably, in comparison to the CT-based Residual Convolutional Neural Network (RCNN) model, the MRCNN shows an improvement of 4. 4% in specificity and 9. 0% in accuracy.
Robust, fast and accurate mapping of diffusional mean kurtosis
In summary, our findings suggest robust, fast and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion weighted magnetic resonance imaging data acquisition time.
Instant tissue field and magnetic susceptibility mapping from MR raw phase using Laplacian enabled deep neural networks
In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the novel neural networks.
CNNs and GANs in MRI-based cross-modality medical image estimation
Generative adversarial networks (GANs) use CNNs as generators and estimated images are discriminated as true or false based on an additional network.
Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks
Purpose: The purpose of this study is to demonstrate a method for Specific Absorption Rate (SAR) reduction for T2-FLAIR MRI sequences at 7T by predicting the required adiabatic pulse power and scaling the amplitude in a slice-wise fashion.
Linear centralization classifier
A classification algorithm, called the Linear Centralization Classifier (LCC), is introduced.
