A Sparse Bayesian Learning Algorithm for Longitudinal Image Data

Sabuncu, Mert R.

doi:10.1007/978-3-319-24574-4_49

Cited by 3 publications

(2 citation statements)

References 14 publications

(20 reference statements)

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“…For an efficient optimization of the model, in practice, we will work in the dual space updating the Equations ( 10) to (18). However, when the model convergence is reached, we can obtain the approximate posterior distribution of w as:…”

Section: Variational Inferencementioning

confidence: 99%

“…Nevertheless, in neuroimaging, we have to deal with large datasets, where the number of cases is significantly smaller than the number of variables, and many of these approaches fail in this scenario, tending to over-fit. To avoid this problem, some authors propose Bayesian approaches but work over a reduced set of features [16][17][18], whereas others point to the use of more refined techniques better adapted to the problem needs [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data

et al. 2022

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In this paper, we propose a novel Machine Learning Model based on Bayesian Linear Regression intended to deal with the low sample-to-variable ratio typically found in neuroimaging studies and focusing on mental disorders. The proposed model combines feature selection capabilities with a formulation in the dual space which, in turn, enables efficient work with neuroimaging data. Thus, we have tested the proposed algorithm with real MRI data from an animal model of schizophrenia. The results show that our proposal efficiently predicts the diagnosis and, at the same time, detects regions which clearly match brain areas well-known to be related to schizophrenia.

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Section: Variational Inferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Bayesian Linear Regression Model for the Analysis of Neuroimaging Data

et al. 2022

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Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs

Ouyang

Zhao

Sullivan

et al. 2021

IEEE J. Biomed. Health Inform.

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Most neurological diseases are characterized by gradual deterioration of brain structure and function. To identify the impact of such diseases, studies have been acquiring large longitudinal MRI datasets and applied deep-learning to predict diagnosis label(s). These learning models apply Convolutional Neural Networks (CNN) to extract informative features from each time point of the longitudinal MRI and Recurrent Neural Networks (RNN) to classify each time point based on those features. However, they neglect the progressive nature of the disease, which may result in clinically implausible predictions across visits. In this paper, we propose a framework that injects the extracted features from CNNs at each time point to the RNN cells considering the dependencies across different time points in the longitudinal data. On the feature level, we propose a novel longitudinal pooling layer to couple features of a visit with those of proceeding ones. On the prediction level, we add a consistency regularization to the classification objective in line with the nature of the disease progression across visits. We evaluate the proposed method on the longitudinal structural MRIs from three neuroimaging datasets: Alzheimers Disease Neuroimaging Initiative (ADNI, N = 404), a dataset composed of 274 healthy controls and 329 patients with Alcohol Use Disorder (AUD), and 255 youths from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). All three experiments show that our method is superior to the widely used methods. The code is available at https://github.com/ouyangjiahong/longitudinal-pooling.

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