Applications of neural networks to medical signal processing

Graupe, Daniel; Liu, R.W.; Moschytz, G.S.

doi:10.1109/cdc.1988.194325

Cited by 16 publications

(2 citation statements)

References 13 publications

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“…Deep networks can be constructed by stacking convolutions, pooling operators and point-wise nonlinearities such as rectified linear activation functions. CNNs have been intensively applied to vision and image recognition problems ( Farabet et al, 2010 , Matusugu et al, 2013 , Ciresan et al, 2013 , Behnke, 2003 , Yaniv et al, 2015 , Masci et al, 2013 ), document recognition ( LeCun et al, 1998 ) and medical signal processing ( Graupe et al, 1988 , Graupe et al, 1989 ), among numerous other applications. However, the learned CNN transformations have mostly led to very good classification results in cases where a lot of data is available.…”

Section: Discussionmentioning

confidence: 99%

3D scattering transforms for disease classification in neuroimaging

Adel

Cohen

Caan

et al. 2017

NeuroImage: Clinical

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Classifying neurodegenerative brain diseases in MRI aims at correctly assigning discrete labels to MRI scans. Such labels usually refer to a diagnostic decision a learner infers based on what it has learned from a training sample of MRI scans. Classification from MRI voxels separately typically does not provide independent evidence towards or against a class; the information relevant for classification is only present in the form of complicated multivariate patterns (or “features”). Deep learning solves this problem by learning a sequence of non-linear transformations that result in feature representations that are better suited to classification. Such learned features have been shown to drastically outperform hand-engineered features in computer vision and audio analysis domains. However, applying the deep learning approach to the task of MRI classification is extremely challenging, because it requires a very large amount of data which is currently not available. We propose to instead use a three dimensional scattering transform, which resembles a deep convolutional neural network but has no learnable parameters. Furthermore, the scattering transform linearizes diffeomorphisms (due to e.g. residual anatomical variability in MRI scans), making the different disease states more easily separable using a linear classifier. In experiments on brain morphometry in Alzheimer's disease, and on white matter microstructural damage in HIV, scattering representations are shown to be highly effective for the task of disease classification. For instance, in semi-supervised learning of progressive versus stable MCI, we reach an accuracy of 82.7%. We also present a visualization method to highlight areas that provide evidence for or against a certain class, both on an individual and group level.

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Section: Discussionmentioning

confidence: 99%

3D scattering transforms for disease classification in neuroimaging

Adel

Cohen

Caan

et al. 2017

NeuroImage: Clinical

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“…Artificial neural networks has been the most popular tools for machine learning [4], which in more general sense for deep learning. Among several deep learning architectures, stacked denoising autoencoders [5], deep belief networks [6][7], and convolutional neural networks [8][9][10][11][12][13] are three of the most popular architectures utilized for different type of applications. Convolutional neural networks (CNNs) are a special kind of deep learning method, CNNs run much faster on GPUs, such as NVidia's Tesla K80 processor, and has achieved state of the art performance on various computer vision tasks, such as object detection, recognition, retrieval, annotation, image classification, and segmentation [14][15][16].…”

Section: Convolutional Neural Network (Cnns) For Nr-iqamentioning

confidence: 99%

Improve of contrast-distorted image quality assessment based on convolutional neural networks

Ahmed

Chen

Jamil

et al. 2019

IJECE

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<span lang="EN-US">Many image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for Contrast Distorted Images (CDI), e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQA-CDI). The existing NR-IQA-CDI relies on features designed by human or handcrafted features because considerable level of skill, domain expertise and efforts are required to design good handcrafted features. Recently, there is great advancement in machine learning with the introduction of deep learning through Convolutional Neural Networks (CNN) which enable machine to learn good features from raw image automatically without any human intervention. Therefore, it is tempting to explore the ways to transform the existing NR-IQA-CDI from using handcrafted features to machine-crafted features using deep learning, specifically Convolutional Neural Networks (CNN).The results show that NR-IQA-CDI based on non-pre-trained CNN (NR-IQA-CDI-NonPreCNN) significantly outperforms those which are based on handcrafted features. In addition to showing best performance, NR-IQA-CDI-NonPreCNN also enjoys the advantage of zero human intervention in designing feature, making it the most attractive solution for NR-IQA-CDI.</span>

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