Bayesian inference by reversible jump MCMC for clustering based on finite generalized inverted Dirichlet mixtures

Bourouis, Sami; Al-Osaimi, Faisal R.; Bouguila, Nizar; Sallay, Hassen; Aldosari, Fahd; Mashrgy, Mohamed Al

doi:10.1007/s00500-018-3244-4

Cited by 17 publications

(10 citation statements)

References 90 publications

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“…Therefore, a better solution especially for our case (i.e., when dealing with complex medical noisy data including COVID-19 infection) is to develop a more robust alternative based on fully Bayesian inference approach. We recall that Bayesian estimation has attracted a lot of attention for many applications [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. It is also known that the Bayesian approach may be more practical due to the existance of powerful simulation techniques like MCMC [ 29 ].…”

Section: Motivationsmentioning

confidence: 99%

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

2021

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Early diagnosis and assessment of fatal diseases and acute infections on chest X-ray (CXR) imaging may have important therapeutic implications and reduce mortality. In fact, many respiratory diseases have a serious impact on the health and lives of people. However, certain types of infections may include high variations in terms of contrast, size and shape which impose a real challenge on classification process. This paper introduces a new statistical framework to discriminate patients who are either negative or positive for certain kinds of virus and pneumonia. We tackle the current problem via a fully Bayesian approach based on a flexible statistical model named shifted-scaled Dirichlet mixture models (SSDMM). This mixture model is encouraged by its effectiveness and robustness recently obtained in various image processing applications. Unlike frequentist learning methods, our developed Bayesian framework has the advantage of taking into account the uncertainty to accurately estimate the model parameters as well as the ability to solve the problem of overfitting. We investigate here a Markov Chain Monte Carlo (MCMC) estimator, which is a computer–driven sampling method, for learning the developed model. The current work shows excellent results when dealing with the challenging problem of biomedical image classification. Indeed, extensive experiments have been carried out on real datasets and the results prove the merits of our Bayesian framework.

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Section: Motivationsmentioning

confidence: 99%

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

2021

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“…They are increasingly popular thanks to their flexibility in including prior information when estimating the model's parameters. It is noted that several Bayesian approximation techniques have been adopted to date such as the Laplace estimator [29] and Markov chain Monte Carlo (MCMC) simulation techniques [30,31]. In particular, the MCMC methods simulate required estimates by running appropriate Markov chains using for instance the Gibbs sampler.…”

Section: Bayesian Learningmentioning

confidence: 99%

Online Learning of Finite and Infinite Gamma Mixture Models for COVID-19 Detection in Medical Images

2020

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The accurate detection of abnormalities in medical images (like X-ray and CT scans) is a challenging problem due to images’ blurred boundary contours, different sizes, variable shapes, and uneven density. In this paper, we tackle this problem via a new effective online variational learning model for both mixtures of finite and infinite Gamma distributions. The proposed approach takes advantage of the Gamma distribution flexibility, the online learning scalability, and the variational inference efficiency. Three different batch and online learning methods based on robust texture-based feature extraction are proposed. Our work is evaluated and validated on several real challenging data sets for different kinds of pneumonia infection detection. The obtained results are very promising given that we approach the classification problem in an unsupervised manner. They also confirm the superiority of the Gamma mixture model compared to the Gaussian mixture model for medical images’ classification.

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“…In particular, finite mixture models have attracted great interest among other approaches [25,27,28] [29]. Recently, some developed mixtures were applied successfully in the case of forgery detection problem [21,30]. In this context, we address the problem of image forgery detection by investigating recent developed mixture model named finite bounded generalized Gaussian mixtures (BGGMM) [31,32].…”

Section: Inpainting Forgery Detectionmentioning

confidence: 99%

Inpainting forgery detection using hybrid generative/discriminative approach based on bounded generalized Gaussian mixture model

Alharbi

Alhakami

Bourouis

et al. 2020

ACI

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We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

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Bayesian inference by reversible jump MCMC for clustering based on finite generalized inverted Dirichlet mixtures

Cited by 17 publications

References 90 publications

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Bayesian Learning of Shifted-Scaled Dirichlet Mixture Models and Its Application to Early COVID-19 Detection in Chest X-ray Images

Online Learning of Finite and Infinite Gamma Mixture Models for COVID-19 Detection in Medical Images

Inpainting forgery detection using hybrid generative/discriminative approach based on bounded generalized Gaussian mixture model

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