4,926 research outputs found
Non-Learning based Deep Parallel MRI Reconstruction (NLDpMRI)
Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand
and scan time directly depends on the number of acquired k-space samples.
Recently, the deep learning-based MRI reconstruction techniques were suggested
to accelerate MR image acquisition. The most common issues in any deep
learning-based MRI reconstruction approaches are generalizability and
transferability. For different MRI scanner configurations using these
approaches, the network must be trained from scratch every time with new
training dataset, acquired under new configurations, to be able to provide good
reconstruction performance. Here, we propose a new generalized parallel imaging
method based on deep neural networks called NLDpMRI to reduce any structured
aliasing ambiguities related to the different k-space undersampling patterns
for accelerated data acquisition. Two loss functions including non-regularized
and regularized are proposed for parallel MRI reconstruction using deep network
optimization and we reconstruct MR images by optimizing the proposed loss
functions over the network parameters. Unlike any deep learning-based MRI
reconstruction approaches, our method doesn't include any training step that
the network learns from a large number of training samples and it only needs
the single undersampled multi-coil k-space data for reconstruction. Also, the
proposed method can handle k-space data with different undersampling patterns,
and the different number of coils. Experimental results show that the proposed
method outperforms the current state-of-the-art GRAPPA method and the deep
learning-based variational network method
A systematic study of the class imbalance problem in convolutional neural networks
In this study, we systematically investigate the impact of class imbalance on
classification performance of convolutional neural networks (CNNs) and compare
frequently used methods to address the issue. Class imbalance is a common
problem that has been comprehensively studied in classical machine learning,
yet very limited systematic research is available in the context of deep
learning. In our study, we use three benchmark datasets of increasing
complexity, MNIST, CIFAR-10 and ImageNet, to investigate the effects of
imbalance on classification and perform an extensive comparison of several
methods to address the issue: oversampling, undersampling, two-phase training,
and thresholding that compensates for prior class probabilities. Our main
evaluation metric is area under the receiver operating characteristic curve
(ROC AUC) adjusted to multi-class tasks since overall accuracy metric is
associated with notable difficulties in the context of imbalanced data. Based
on results from our experiments we conclude that (i) the effect of class
imbalance on classification performance is detrimental; (ii) the method of
addressing class imbalance that emerged as dominant in almost all analyzed
scenarios was oversampling; (iii) oversampling should be applied to the level
that completely eliminates the imbalance, whereas the optimal undersampling
ratio depends on the extent of imbalance; (iv) as opposed to some classical
machine learning models, oversampling does not cause overfitting of CNNs; (v)
thresholding should be applied to compensate for prior class probabilities when
overall number of properly classified cases is of interest
Deep Learning Enabled Real Time Speckle Recognition and Hyperspectral Imaging using a Multimode Fiber Array
We demonstrate the use of deep learning for fast spectral deconstruction of
speckle patterns. The artificial neural network can be effectively trained
using numerically constructed multispectral datasets taken from a measured
spectral transmission matrix. Optimized neural networks trained on these
datasets achieve reliable reconstruction of both discrete and continuous
spectra from a monochromatic camera image. Deep learning is compared to
analytical inversion methods as well as to a compressive sensing algorithm and
shows favourable characteristics both in the oversampling and in the sparse
undersampling (compressive) regimes. The deep learning approach offers
significant advantages in robustness to drift or noise and in reconstruction
speed. In a proof-of-principle demonstrator we achieve real time recovery of
hyperspectral information using a multi-core, multi-mode fiber array as a
random scattering medium.Comment: 12 pages, 6 figures + Appendix of 5 pages and 5 figure
Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data
In this work we reduce undersampling artefacts in two-dimensional ()
golden-angle radial cine cardiac MRI by applying a modified version of the
U-net. We train the network on spatio-temporal slices which are previously
extracted from the image sequences. We compare our approach to two and a
Deep Learning-based post processing methods and to three iterative
reconstruction methods for dynamic cardiac MRI. Our method outperforms the
spatially trained U-net and the spatio-temporal U-net. Compared to the
spatio-temporal U-net, our method delivers comparable results, but with
shorter training times and less training data. Compared to the Compressed
Sensing-based methods -FOCUSS and a total variation regularised
reconstruction approach, our method improves image quality with respect to all
reported metrics. Further, it achieves competitive results when compared to an
iterative reconstruction method based on adaptive regularization with
Dictionary Learning and total variation, while only requiring a small fraction
of the computational time. A persistent homology analysis demonstrates that the
data manifold of the spatio-temporal domain has a lower complexity than the
spatial domain and therefore, the learning of a projection-like mapping is
facilitated. Even when trained on only one single subject without
data-augmentation, our approach yields results which are similar to the ones
obtained on a large training dataset. This makes the method particularly
suitable for training a network on limited training data. Finally, in contrast
to the spatial U-net, our proposed method is shown to be naturally robust
with respect to image rotation in image space and almost achieves
rotation-equivariance where neither data-augmentation nor a particular network
design are required.Comment: To be published in IEEE Transactions on Medical Imagin
Broad Neural Network for Change Detection in Aerial Images
A change detection system takes as input two images of a region captured at
two different times, and predicts which pixels in the region have undergone
change over the time period. Since pixel-based analysis can be erroneous due to
noise, illumination difference and other factors, contextual information is
usually used to determine the class of a pixel (changed or not). This
contextual information is taken into account by considering a pixel of the
difference image along with its neighborhood. With the help of ground truth
information, the labeled patterns are generated. Finally, Broad Learning
classifier is used to get prediction about the class of each pixel. Results
show that Broad Learning can classify the data set with a significantly higher
F-Score than that of Multilayer Perceptron. Performance comparison has also
been made with other popular classifiers, namely Multilayer Perceptron and
Random Forest.Comment: : IEMGraph (International Conference on
Emerging Technology in Modelling and Graphics) 2018 : 6-7 September, 2018 :
Kolkatta, Indi
Introducing DeepBalance: Random Deep Belief Network Ensembles to Address Class Imbalance
Class imbalance problems manifest in domains such as financial fraud
detection or network intrusion analysis, where the prevalence of one class is
much higher than another. Typically, practitioners are more interested in
predicting the minority class than the majority class as the minority class may
carry a higher misclassification cost. However, classifier performance
deteriorates in the face of class imbalance as oftentimes classifiers may
predict every point as the majority class. Methods for dealing with class
imbalance include cost-sensitive learning or resampling techniques. In this
paper, we introduce DeepBalance, an ensemble of deep belief networks trained
with balanced bootstraps and random feature selection. We demonstrate that our
proposed method outperforms baseline resampling methods such as SMOTE and
under- and over-sampling in metrics such as AUC and sensitivity when applied to
a highly imbalanced financial transaction data. Additionally, we explore
performance and training time implications of various model parameters.
Furthermore, we show that our model is easily parallelizable, which can reduce
training times. Finally, we present an implementation of DeepBalance in R
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
Deep learning approaches have shown promising performance for compressed
sensing-based Magnetic Resonance Imaging. While deep neural networks trained
with mean squared error (MSE) loss functions can achieve high peak signal to
noise ratio, the reconstructed images are often blurry and lack sharp details,
especially for higher undersampling rates. Recently, adversarial and perceptual
loss functions have been shown to achieve more visually appealing results.
However, it remains an open question how to (1) optimally combine these loss
functions with the MSE loss function and (2) evaluate such a perceptual
enhancement. In this work, we propose a hybrid method, in which a visual
refinement component is learnt on top of an MSE loss-based reconstruction
network. In addition, we introduce a semantic interpretability score, measuring
the visibility of the region of interest in both ground truth and reconstructed
images, which allows us to objectively quantify the usefulness of the image
quality for image post-processing and analysis. Applied on a large cardiac MRI
dataset simulated with 8-fold undersampling, we demonstrate significant
improvements () over the state-of-the-art in both a human observer
study and the semantic interpretability score.Comment: To be published at MICCAI 201
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Following the success of deep learning in a wide range of applications,
neural network-based machine learning techniques have received interest as a
means of accelerating magnetic resonance imaging (MRI). A number of ideas
inspired by deep learning techniques from computer vision and image processing
have been successfully applied to non-linear image reconstruction in the spirit
of compressed sensing for both low dose computed tomography and accelerated
MRI. The additional integration of multi-coil information to recover missing
k-space lines in the MRI reconstruction process, is still studied less
frequently, even though it is the de-facto standard for currently used
accelerated MR acquisitions. This manuscript provides an overview of the recent
machine learning approaches that have been proposed specifically for improving
parallel imaging. A general background introduction to parallel MRI is given
that is structured around the classical view of image space and k-space based
methods. Both linear and non-linear methods are covered, followed by a
discussion of recent efforts to further improve parallel imaging using machine
learning, and specifically using artificial neural networks. Image-domain based
techniques that introduce improved regularizers are covered as well as k-space
based methods, where the focus is on better interpolation strategies using
neural networks. Issues and open problems are discussed as well as recent
efforts for producing open datasets and benchmarks for the community.Comment: 14 pages, 7 figure
Clustering and Learning from Imbalanced Data
A learning classifier must outperform a trivial solution, in case of
imbalanced data, this condition usually does not hold true. To overcome this
problem, we propose a novel data level resampling method - Clustering Based
Oversampling for improved learning from class imbalanced datasets. The
essential idea behind the proposed method is to use the distance between a
minority class sample and its respective cluster centroid to infer the number
of new sample points to be generated for that minority class sample. The
proposed algorithm has very less dependence on the technique used for finding
cluster centroids and does not effect the majority class learning in any way.
It also improves learning from imbalanced data by incorporating the
distribution structure of minority class samples in generation of new data
samples. The newly generated minority class data is handled in a way as to
prevent outlier production and overfitting. Implementation analysis on
different datasets using deep neural networks as the learning classifier shows
the effectiveness of this method as compared to other synthetic data resampling
techniques across several evaluation metrics.Comment: 9 pages, To Appear at NIPS 2018 Workshop
Devising Malware Characterstics using Transformers
With the increasing number of cybersecurity threats, it becomes more
difficult for researchers to skim through the security reports for malware
analysis. There is a need to be able to extract highly relevant sentences
without having to read through the entire malware reports. In this paper, we
are finding relevant malware behavior mentions from Advanced Persistent Threat
Reports. This main contribution is an opening attempt to Transformer the
approach for malware behavior analysis.Comment: 5 pages, 3 figures, 3 table
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