Scientific Reports (2020).
Abstract: Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurrence. In this study, we proposed to use a multiparametric MRI data as a sequence input for the convolutional neural network with the recurrent neural network based deep learning structure to discriminate between pseudoprogression and true tumor progression.
Scientific Reports (2020).
Abstract: Diabetic retinopathy (DR) is a severe retinal disorder that can lead to vision loss, however, its underlying mechanism has not been fully understood. Previous studies have taken advantage of Optical Coherence Tomography (OCT) and shown that the thickness of individual retinal layers are affected in patients with DR. However, most studies analyzed the thickness by calculating summary statistics from retinal thickness maps of the macula region. This study aims to apply a density function-based statistical framework to the thickness data obtained through OCT, and to compare the predictive power of various retinal layers to assess the severity of DR.
NeuroImage: Clinical (2020).
Abstract: In cancer radiomics, textural features evaluated from image intensity-derived gray-level co-occurrence matrices (GLCMs) have been studied to evaluate gray-level spatial dependence within the regions of interest in the brain. Most of these analysis work with summary statistics (or texture-based features) constructed using the GLCM entries, and potentially overlook other structural properties in the GLCM. In our proposed Bayesian framework, we treat each GLCM as a realization of a two-dimensional stochastic functional process observed with error at discrete time points.
Invited for Statistical Analysis and Data Mining Best Paper Session at Joint Statistical Meetings 2022. (JSM 2022 Program)
Statistical Analysis and Data Mining: The ASA Data Science Journal (2020).
Abstract: With the advent of modern technologies, it is increasingly common to deal with data of large dimensions in various scientific fields of study. In this paper, we develop a Bayesian approach for the classification of multi-subject high-dimensional electroencephalography (EEG) data. In this EEG data, we have a matrix of covariates corresponding to each subject from either the alcoholic or control group.
Harvard Data Science Review (2020).
Abstract: With only 536 COVID‑19 cases and 11 fatalities, India took the historic decision of a 21‑day national lockdown on March 25, 2020. The lockdown was first extended to May 3 soon after the analysis of this article was completed, and then to May 18 while this article was being revised. In this article, we use a Bayesian extension of the susceptible‑infected‑removed (eSIR) model designed for intervention forecasting to study the short‑ and long‑term impact of an initial 21‑day lockdown on the total number of COVID‑19 infections in India compared to other, less severe nonpharmaceutical interventions.
Journal of the Royal Statistical Society: Series C (Applied Statistics) (2019).
Abstract: Because of the immense technological advances, very often we encounter data in high dimensions. Any set of measurements taken at multiple time points for multiple subjects leads to data of more than two dimensions (a matrix of covariates for each subject). We present a Bayesian variable‐selection method to identify the active regions in the brain as a response to a certain stimulus.
Journal of Biostatistics and Epidemiology (2019).
Abstract: Background and Aim: We aim to build a classifier to distinguish between malaria-infected red blood cells (RBCs) and healthy cells using the two-dimensional (2D) microscopic images of RBCs. We demonstrate the process of cell segmentation and feature extraction from the 2D images.
Methods and Materials: We describe an approach to address the problem using mixture discriminant analysis (MDA) on the 2D image profiles of the RBCs.