Deep Residual Autoencoders for Expectation Maximization-Inspired Dictionary Learning

Tolooshams, Bahareh; Dey, S.; Ba, Demba

doi:10.1109/tnnls.2020.3005348

Cited by 27 publications

(16 citation statements)

References 39 publications

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“…The SOM, commonly known as the Kohonen map, is a pattern exploration and visualization model for high-dimensional datasets. Teuvo Kalevi Kohonen was the first to create this pattern in 1982 [ 42 ]. SOM is a clustering methodology that provides conventional statistical methods to identify groups in a dataset.…”

Section: Basic Concepts and Corresponding Terminologiesmentioning

confidence: 99%

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Heidari¹,

Navimipour²,

Ünal³

et al. 2022

Computers in Biology and Medicine

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Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.

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Section: Basic Concepts and Corresponding Terminologiesmentioning

confidence: 99%

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Heidari¹,

Navimipour²,

Ünal³

et al. 2022

Computers in Biology and Medicine

View full text Add to dashboard Cite

show abstract

“…where λ is a sparsity-enforcing parameter, and the norm constraints are to avoid scaling ambiguity. Following a similar approach to [17,23,24], we construct an autoencoder where its encoder maps z n into a sparse filter x n by unfolding T iterations of a variant of accelerated proximal gradient algorithm, FISTA [22], for sparse recovery. Specifically, each unfolding layer performs the following iteration…”

Section: Network Architecturementioning

confidence: 99%

“…Recent works proposed model-based neural networks to address computational efficiency [17,18], but still require full measurements for recovery. To enable compression, Chang et al [19] proposed an autoencoder, called RandNet, for dictionary learning.…”

Section: Introductionmentioning

confidence: 99%

Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution

Tolooshams

Mulleti

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution. In this problem, each channel's measurements are given as convolution of a common source signal and sparse filter. Unlike prior works where the compression is achieved either through random projections or by applying a fixed structured compression matrix, this paper proposes to learn the compression matrix from data. Given the full measurements, the proposed network is trained in an unsupervised fashion to learn the source and estimate sparse filters. Then, given the estimated source, we learn a structured compression operator while optimizing for signal reconstruction and sparse filter recovery. The efficient structure of the compression allows its practical hardware implementation. The proposed neural network is an autoencoder constructed based on an unfolding approach: upon training, the encoder maps the compressed measurements into an estimate of sparse filters using the compression operator and the source, and the linear convolutional decoder reconstructs the full measurements. We demonstrate that our method is superior to classical structured compressive sparse multichannel blind-deconvolution methods in terms of accuracy and speed of sparse filter recovery.

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“…Since they focus on unrolling the multiplicative update (MU) algorithm (see also the earlier work of (Hershey et al, 2014) in this context), their network structure is quite different from both (Xiong et al, 2021) and the algorithm we investigate in this work. Apart from the above articles, we would like to highlight that several works about unrolling methods for related-to-BSS problems, such as dictionary learning (Tolooshams et al, 2020) or even Magnetic Resonance Imaging (Arvinte et al, 2021), exist. However, as such problems have different purposes and challenges, it is beyond the scope of this article to review them.…”

Section: Related Workmentioning

confidence: 99%

Unrolling PALM for sparse semi-blind source separation

Fahes¹,

Kervazo²,

Bobin³

et al. 2021

Preprint

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Sparse Blind Source Separation (BSS) has become a well established tool for a wide range of applications -for instance, in astrophysics and remote sensing. Classical sparse BSS methods, such as the Proximal Alternating Linearized Minimization (PALM) algorithm, nevertheless often suffer from a difficult hyperparameter choice, which undermines their results. To bypass this pitfall, we propose in this work to build on the thriving field of algorithm unfolding/unrolling. Unrolling PALM enables to leverage the data-driven knowledge stemming from realistic simulations or ground-truth data by learning both PALM hyperparameters and variables. In contrast to most existing unrolled algorithms, which assume a fixed known dictionary during the training and testing phases, this article further emphasizes on the ability to deal with variable mixing matrices (a.k.a. dictionaries). The proposed Learned PALM (LPALM) algorithm thus enables to perform semi-blind source separation, which is key to increase the generalization of the learnt model in real-world applications. We illustrate the relevance of LPALM in astrophysical multispectral imaging: the algorithm not only needs up to 10 4 − 10 5 times fewer iterations than PALM, but also improves the separation quality, while avoiding the cumbersome hyperparameter and initialization choice of PALM. We further show that LPALM outperforms other unrolled source separation methods in the semi-blind setting.

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Deep Residual Autoencoders for Expectation Maximization-Inspired Dictionary Learning

Cited by 27 publications

References 39 publications

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution

Unrolling PALM for sparse semi-blind source separation

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