Spatial Signal Detection Using Continuous Shrinkage Priors

Jhuang, An-Ting; Fuentes, Montserrat; Jones, Jacob L.; Esteves, Giovanni; Fancher, Chris M.; Furman, Marschall; Reich, Brian J.

doi:10.1080/00401706.2018.1546622

Cited by 7 publications

(6 citation statements)

References 148 publications

(148 reference statements)

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“…For instance, [31] proposed an innovative approach named sparse Kronecker product decomposition to locate informative signal regions. From Bayesian perspectives, the ROI detection was approached by modeling the image coefficients using certain prior distributions [8,12,13,15]. Beyond region detection, image regression has also been considered under other settings, such as [6,29,37], and so forth.…”

Section: Introductionmentioning

confidence: 99%

Individualized image region detection with total variation

Wu,

Wang,

Feng

2024

Statistical Analysis

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Medical image data have emerged to be an indispensable component of modern medicine. Different from many general image problems that focus on outcome prediction or image recognition, medical image analysis pays more attention to model interpretation. For instance, given a list of medical images and corresponding labels of patients' health status, it is often of greater importance to identify the image regions that could differentiate the outcome status, compared to simply predicting labels of new images. Moreover, medical image data often demonstrate strong individual heterogeneity. In other words, the image regions associated with an outcome could be different across patients. As a consequence, the traditional one‐model‐fits‐all approach not only omits patient heterogeneity but also possibly leads to misleading or even wrong conclusions. In this article, we introduce a novel statistical framework to detect individualized regions that are associated with a binary outcome, that is, whether a patient has a certain disease or not. Moreover, we propose a total variation‐based penalization for individualized image region detection under a local label‐free scenario. Considering that local labeling is often difficult to obtain for medical image data, our approach may potentially have a wider range of applications in medical research. The effectiveness of our proposed approach is validated by two real histopathology databases: Colon Cancer and Camelyon16.

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Section: Introductionmentioning

confidence: 99%

Individualized image region detection with total variation

Wu,

Wang,

Feng

2024

Statistical Analysis

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“…The most related studies are from Bayesian perspectives, where regression coefficient is first vectorized and then modeled with certain prior distributions to detect signal regions. For example, the Ising prior is used in Goldsmith et al (2014) and Li et al (2015); the soft-thresholdings of a latent Gaussian process is proposed by Kang et al (2018); and continuous shrinkage priors is applied on Jhuang et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Sparse Kronecker Product Decomposition: A General Framework of Signal Region Detection in Image Regression

Wu¹,

Feng²

2022

Preprint

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This paper aims to present the first Frequentist framework on signal region detection in high-resolution and high-order image regression problems. Image data and scalar-on-image regression are intensively studied in recent years. However, most existing studies on such topics focused on outcome prediction, while the research on image region detection is rather limited, even though the latter is often more important. In this paper, we develop a general framework named Sparse Kronecker Product Decomposition (SKPD) to tackle this issue. The SKPD framework is general in the sense that it works for both matrices (e.g., 2D grayscale images) and (high-order) tensors (e.g., 2D colored images, brain MRI/fMRI data) represented image data. Moreover, unlike many Bayesian approaches, our framework is computationally scalable for high-resolution image problems. Specifically, our framework includes: 1) the one-term SKPD; 2) the multiterm SKPD; and 3) the nonlinear SKPD. We propose nonconvex optimization problems to estimate the one-term and multi-term SKPDs and develop path-following algorithms for the nonconvex optimization.The computed solutions of the path-following algorithm are guaranteed to converge to the truth with a particularly chosen initialization even though the optimization is nonconvex. Moreover, the region detection consistency could also be guaranteed by the one-term and multi-term SKPD. The nonlinear SKPD is highly connected to shallow convolutional neural networks (CNN), particular to CNN with one convolutional layer and one fully connected layer. Effectiveness of SKPDs is validated by real brain imaging data in the UK Biobank database.

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“…Noh and Park (2010), Tang et al (2013) considered a varying co-efficient model that accounts for sparsity but not smoothness. To the best of our knowledge, only Boehm-Vock et al (2015) and Jhuang et al (2018) consider both smoothness and sparsity for image-on-image regression. Their methodology captures the spatial dependence using copulas, which is computationally expensive for large datasets.…”

Section: Introductionmentioning

confidence: 99%

Spatial Shrinkage Via the Product Independent Gaussian Process Prior

Roy

Reich

Guinness

et al. 2021

Journal of Computational and Graphical Statistics

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We study the problem of sparse signal detection on a spatial domain. We propose a novel approach to model continuous signals that are sparse and piecewise smooth as product of independent Gaussian processes (PING) with a smooth covariance kernel. The smoothness of the PING process is ensured by the smoothness of the covariance kernels of Gaussian components in the product, and sparsity is controlled by the number of components. The bivariate kurtosis of the PING process shows more components in the product results in thicker tail and sharper peak at zero. The simulation results demonstrate the improvement in estimation using the PING prior over Gaussian process (GP) prior for different image regressions. We apply our method to a longitudinal MRI dataset to detect the regions that are affected by multiple sclerosis (MS) in the greatest magnitude through an image-on-scalar regression model. Due to huge dimensionality of these images, we transform the data into the spectral domain and develop methods to conduct computation in this domain. In our MS imaging study, the estimates from the PING model are more informative than those from the GP model.

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Spatial Signal Detection Using Continuous Shrinkage Priors

Cited by 7 publications

References 148 publications

Individualized image region detection with total variation

Individualized image region detection with total variation

Sparse Kronecker Product Decomposition: A General Framework of Signal Region Detection in Image Regression

Spatial Shrinkage Via the Product Independent Gaussian Process Prior

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