no code implementations • 16 Apr 2024 • Soumen Ghosh, Viktor Vegh, Shahrzad Moinian, Hamed Moradi, Alice-Ann Sullivan, John Phamnguyen, David Reutens
State-of-the-art classification algorithms were trained and tested using NeuroMorphix features to predict seizure recurrence.
no code implementations • 8 Jan 2024 • Hanem Ellethy, Shekhar S. Chandra, Viktor Vegh
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.
no code implementations • 23 Nov 2023 • Hanem Ellethy, Shekhar S. Chandra, Viktor Vegh
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.
no code implementations • 22 Sep 2023 • Hanem Ellethy, Viktor Vegh, Shekhar S. Chandra
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.
no code implementations • 30 Nov 2022 • Megan E. Farquhar, Qianqian Yang, Viktor Vegh
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.
2 code implementations • 15 Nov 2021 • Yang Gao, Zhuang Xiong, Amir Fazlollahi, Peter J Nestor, Viktor Vegh, Fatima Nasrallah, Craig Winter, G. Bruce Pike, Stuart Crozier, Feng Liu, Hongfu Sun
In addition, experiments on patients with intracranial hemorrhage and multiple sclerosis were also performed to test the generalization of the novel neural networks.
no code implementations • 4 Jun 2021 • Azin Shokraei Fard, David C. Reutens, Viktor Vegh
Generative adversarial networks (GANs) use CNNs as generators and estimated images are discriminated as true or false based on an additional network.
1 code implementation • 19 Nov 2019 • Shahrokh Abbasi-Rad, Kieran O'Brien, Samuel Kelly, Viktor Vegh, Anders Rodell, Yasvir Tesiram, Jin Jin, Markus Barth, Steffen Bollmann
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.
no code implementations • 22 Dec 2017 • Mohammad Reza Bonyadi, Viktor Vegh, David C. Reutens
A classification algorithm, called the Linear Centralization Classifier (LCC), is introduced.