AIMC Topic: Magnetic Resonance Imaging
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An Analysis of the Vulnerability of Two Common Deep Learning-Based Medical Image Segmentation Techniques to Model Inversion Attacks.
Sensors (Basel, Switzerland)
Jun 4, 2021
Recent research in computer vision has shown that original images used for training of deep learning models can be reconstructed using so-called inversion attacks. However, the feasibility of this attack type has not been investigated for complex 3D ...
DBAN: Adversarial Network With Multi-Scale Features for Cardiac MRI Segmentation.
IEEE journal of biomedical and health informatics
Jun 3, 2021
With the development of medical artificial intelligence, automatic magnetic resonance image (MRI) segmentation method is quite desirable. Inspired by the power of deep neural networks, a novel deep adversarial network, dilated block adversarial netwo...
Predicting the Prognosis of MCI Patients Using Longitudinal MRI Data.
IEEE/ACM transactions on computational biology and bioinformatics
Jun 3, 2021
The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing "Mild Cognitive Impairment (MCI) due to Alzheimer's Disease (AD)" patients among a list of MCI patients. In this system we are usin...
MRI Based Radiomics Approach With Deep Learning for Prediction of Vessel Invasion in Early-Stage Cervical Cancer.
IEEE/ACM transactions on computational biology and bioinformatics
Jun 3, 2021
This article aims to build deep learning-based radiomic methods in differentiating vessel invasion from non-vessel invasion in cervical cancer with multi-parametric MRI data. A set of 1,070 dynamic T1 contrast-enhanced (DCE-T1) and 986 T2 weighted im...
D-UNet: A Dimension-Fusion U Shape Network for Chronic Stroke Lesion Segmentation.
IEEE/ACM transactions on computational biology and bioinformatics
Jun 3, 2021
Assessing the location and extent of lesions caused by chronic stroke is critical for medical diagnosis, surgical planning, and prognosis. In recent years, with the rapid development of 2D and 3D convolutional neural networks (CNN), the encoder-decod...
Automatic brain tumour diagnostic method based on a back propagation neural network and an extended set-membership filter.
Computer methods and programs in biomedicine
Jun 2, 2021
BACKGROUND: Diagnosing brain tumours remains a challenging task in clinical practice. Despite their questionable accuracy, magnetic resonance image (MRI) scans are presently considered the optimal facility for assessing the growth of tumours. However...
Automatic recognition of specific local cortical folding patterns.
NeuroImage
Jun 2, 2021
The study of local cortical folding patterns showed links with psychiatric illnesses as well as cognitive functions. Despite the tools now available to visualize cortical folds in 3D, manually classifying local sulcal patterns is a time-consuming and...
k-Space-based coil combination via geometric deep learning for reconstruction of non-Cartesian MRSI data.
Magnetic resonance in medicine
Jun 1, 2021
PURPOSE: State-of-the-art whole-brain MRSI with spatial-spectral encoding and multichannel acquisition generates huge amounts of data, which must be efficiently processed to stay within reasonable reconstruction times. Although coil combination signi...
Editorial for "Image Quality Assessment of Fetal Brain MRI Using Multi-Instance Deep Learning Methods".
Journal of magnetic resonance imaging : JMRI
Jun 1, 2021
Convolutional neural networks to identify malformations of cortical development: A feasibility study.
Seizure
May 31, 2021
OBJECTIVE: To develop and test a deep learning model to automatically detect malformations of cortical development (MCD).
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