scholarly journals A deep descriptor for cross-tasking EEG-based recognition

2021 ◽  
Vol 7 ◽  
pp. e549
Author(s):  
Mariana R.F. Mota ◽  
Pedro H.L. Silva ◽  
Eduardo J.S. Luz ◽  
Gladston J.P. Moreira ◽  
Thiago Schons ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Vital Signs ◽  
Motor Task ◽  
Eeg Signal ◽  
Convolutional Networks ◽  
Motor Movement ◽  
Biometric Verification ◽  
Evaluation Protocol ◽  
Novel Method ◽  
The Impact

Due to the application of vital signs in expert systems, new approaches have emerged, and vital signals have been gaining space in biometrics. One of these signals is the electroencephalogram (EEG). The motor task in which a subject is doing, or even thinking, influences the pattern of brain waves and disturb the signal acquired. In this work, biometrics with the EEG signal from a cross-task perspective are explored. Based on deep convolutional networks (CNN) and Squeeze-and-Excitation Blocks, a novel method is developed to produce a deep EEG signal descriptor to assess the impact of the motor task in EEG signal on biometric verification. The Physionet EEG Motor Movement/Imagery Dataset is used here for method evaluation, which has 64 EEG channels from 109 subjects performing different tasks. Since the volume of data provided by the dataset is not large enough to effectively train a Deep CNN model, it is also proposed a data augmentation technique to achieve better performance. An evaluation protocol is proposed to assess the robustness regarding the number of EEG channels and also to enforce train and test sets without individual overlapping. A new state-of-the-art result is achieved for the cross-task scenario (EER of 0.1%) and the Squeeze-and-Excitation based networks overcome the simple CNN architecture in three out of four cross-individual scenarios.

scholarly journals Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Malte Seemann ◽  
Lennart Bargsten ◽  
Alexander Schlaefer
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Computed Tomography Angiography ◽  
Data Augmentation ◽  
Domain Adaptation ◽  
Synthetic Image ◽  
Wide Range ◽  
The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

scholarly journals Adversarial Data Augmentation on Breast MRI Segmentation

2021 ◽  
Vol 11 (10) ◽  
pp. 4554
Author(s):  
João F. Teixeira ◽  
Mariana Dias ◽  
Eva Batista ◽  
Joana Costa ◽  
Luís F. Teixeira ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Medical Image Analysis ◽  
Synthetic Data ◽  
Breast Mri ◽  
Semantic Segmentation ◽  
Adversarial Networks ◽  
Augmentation Strategy ◽  
Magnetic Resonance Imaging Mri ◽  
Segmentation Task ◽  
The Impact

The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images, with the same annotation shortage issues, for which, to the best of our knowledge, no previous work tried synthesizing data. Within this context, our work addresses the problem of synthesizing breast MRI images from corresponding annotations and evaluate the impact of this data augmentation strategy on a semantic segmentation task. We explored variations of image-to-image translation using conditional GANs, namely fitting the generator’s architecture with residual blocks and experimenting with cycle consistency approaches. We studied the impact of these changes on visual verisimilarity and how an U-Net segmentation model is affected by the usage of synthetic data. We achieved sufficiently realistic-looking breast MRI images and maintained a stable segmentation score even when completely replacing the dataset with the synthetic set. Our results were promising, especially when concerning to Pix2PixHD and Residual CycleGAN architectures.

Clinical Conundrums When Integrating the QbTest into a Standard ADHD Assessment of Children and Young People

2021 ◽  
Author(s):  
Carsten Vogt
Keyword(s):  
Clinical Practice ◽  
Clinical Decision Making ◽  
Ecological Validity ◽  
Neuropsychological Test ◽  
Real Life ◽  
Clinical Decision ◽  
Hyperactivity Disorder ◽  
Standard Clinical Practice ◽  
Novel Method ◽  
The Impact

AbstractThe uptake of the QbTest in clinical practice is increasing and has recently been supported by research evidence proposing its effectiveness in relation to clinical decision-making. However, the exact underlying process leading to this clinical benefit is currently not well established and requires further clarification. For the clinician, certain challenges arise when adding the QbTest as a novel method to standard clinical practice, such as having the skills required to interpret neuropsychological test information and assess for diagnostically relevant neurocognitive domains that are related to attention-deficit hyperactivity disorder (ADHD), or how neurocognitive domains express themselves within the behavioral classifications of ADHD and how the quantitative measurement of activity in a laboratory setting compares with real-life (ecological validity) situations as well as the impact of comorbidity on test results. This article aims to address these clinical conundrums in aid of developing a consistent approach and future guidelines in clinical practice.

scholarly journals Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fahime Khozeimeh ◽  
Danial Sharifrazi ◽  
Navid Hoseini Izadi ◽  
Javad Hassannataj Joloudari ◽  
Afshin Shoeibi ◽  
...  
Keyword(s):  
Clinical Data ◽  
Data Augmentation ◽  
Clinical Information ◽  
Ct Images ◽  
Classification Performance ◽  
Survival Chance ◽  
Average Accuracy ◽  
Novel Method ◽  
Aided Diagnosis ◽  
Augmentation Procedure

AbstractCOVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.

Underwater Acoustic Target Recognition Based on Generative Adversarial Network Data Augmentation

2021 ◽  
Vol 263 (2) ◽  
pp. 4558-4564
Author(s):  
Minghong Zhang ◽  
Xinwei Luo
Keyword(s):  
Data Augmentation ◽  
Target Recognition ◽  
Training Data ◽  
Small Samples ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Underwater Acoustic ◽  
Adversarial Network ◽  
Acoustic Target ◽  
The Impact

Underwater acoustic target recognition is an important aspect of underwater acoustic research. In recent years, machine learning has been developed continuously, which is widely and effectively applied in underwater acoustic target recognition. In order to acquire good recognition results and reduce the problem of overfitting, Adequate data sets are essential. However, underwater acoustic samples are relatively rare, which has a certain impact on recognition accuracy. In this paper, in addition of the traditional audio data augmentation method, a new method of data augmentation using generative adversarial network is proposed, which uses generator and discriminator to learn the characteristics of underwater acoustic samples, so as to generate reliable underwater acoustic signals to expand the training data set. The expanded data set is input into the deep neural network, and the transfer learning method is applied to further reduce the impact caused by small samples by fixing part of the pre-trained parameters. The experimental results show that the recognition result of this method is better than the general underwater acoustic recognition method, and the effectiveness of this method is verified.

A Four Novel Energy Pattern Factor Method for Computation of Weibull Parameter in Impact Strength Reliability of Fibre-Reinforced Concrete

2018 ◽  
Vol 7 (3.12) ◽  
pp. 272
Author(s):  
Gayathri R ◽  
Murali. G ◽  
Parthiban Kathirvel ◽  
Haridharan M.K ◽  
Karthikeyan. K
Keyword(s):  
Reinforced Concrete ◽  
Impact Strength ◽  
Shape Parameter ◽  
Failure Strength ◽  
Fibre Reinforced Concrete ◽  
Test Results ◽  
Weibull Parameter ◽  
Impact Failure ◽  
Novel Method ◽  
The Impact

Impact strength data is a noteworthy factor for designing airport pavements, civilian and military structures etc and it is ought to be modelled precisely. In order to achieve an appropriate modelling data, it is important to select a suitable estimation method. One such commonly used statistical tool is the two parameter Weibull distribution for modelling impact failure strength accurately besides the variations in test results. This study statistically commandsthe variations in the impact failure strength (number of blows to induce failure) of fibre reinforced concrete (FRC) subjected to drop hammer test. Subsequently, a four-different novel method for the computation of Weibull parameter (Shape parameter) based on the earlier researchers test results has been proposed. The accuracy of the proposed four novel method is demonstrated by comparing with power density method and verified with goodness of fit test. Finally, the impact failure strength of FRC is offered in terms of reliability. The proposed four NEPFM is very suitable and efficient to compute the shape parameter in impact failure strength applications. 

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