Deep learning methods for solving linear inverse problems: Research directions and paradigms

The linear inverse problem is fundamental to the development of various scientific areas. Innumerable attempts have been carried out to solve different variants of the linear inverse problem in different applications. Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance in many applications. In this paper, we present a comprehensive survey of the recent progress in the development of deep learning for solving various linear inverse problems. We review how deep learning methods are used in solving different linear inverse problems, and explore the structured neural network architectures that incorporate knowledge used in traditional methods. Furthermore, we identify open challenges and potential future directions along this research line

Image Restoration for Remote Sensing: Overview and Toolbox

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS

Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD

An Adaptive Deep Learning for Causal Inference Based on Support Points With High-Dimensional Data

The Sample splitting method in semiparametric statistics could introduce inconsistency in inference and estimation. Thus, to make adaptive learning based on observational data and establish valid learning that helps in the estimation and inference of the parameters and hyperparameters using double machine learning, this study introduces an efficient sample splitting technique for causal inference in the semiparametric framework, in other words, the support points sample splitting( SPSS), a subsampling method based on the energy distance concept is employed for causal inference under double machine learning paradigm. This work is based on the idea that the support points sample splitting (SPSS) is an optimal representative point of the data in a random sample versus the counterpart of random splitting, which implies that the support points sample splitting is an optimal sub-representation of the underlying data generating distribution. To my best knowledge, the conceptual foundation of the support points-based sample splitting is a cutting-edge method of subsampling and the best representation of a full big data set in the sense that the unit structural information of the underlying distribution via the traditional random data splitting is most likely not preserved. Three estimators were applied for double/debiased machine learning causal inference a paradigm that estimates the causal treatment effect from observational data based on machine learning algorithms with the support points sample splitting (SPSS). This study is considering Support Vector Machine (SVM) and Deep Learning (DL) as the predictive estimators. A comparative study is conducted between the SVM and DL with the support points technique to the benchmark results of Chernozhukov et al. (2018) that used instead, the random forest, the neural network, and the regression trees with random k-fold cross-fitting technique. An ensemble machine learning algorithm is proposed that is a hybrid of the super learner and the deep learning with the support points splitting to compare it to the results of Chernozhukov et al. (2018). Finally, a socio-economic real-world dataset, for the 401(k)-pension plan, is used to investigate and evaluate the proposed methods to those in Chernozhukov et al. (2018). The result of this study was under 162 simulations, shows that the three proposed models converge, support vector machine (SVM) with support points sample splitting (SPSS) under double machine learning (DML), the deep learning (DL) with support points sample splitting under double machine learning (DML), and the hybrid of super learning (SL) and deep learning with support points sample splitting under double machine learning. However, the performance of the three models differs. The first model, support vector machine (SVM) with support points sample splitting (SPSS) under double machine learning (DML) has the lowest performance compared to the other two models. In terms of the quality of the causal estimators, it has a higher MSE and inconsistency of the simulation results on all three data dimension levels, low-high-dimensional (p = 20,50,80), moderate-high-dimensional (p = 100, 200, 500), and big-high-dimensional p = (1000, 2000, 5000). The two other models, deep learning (DL) with support points sample splitting under double machine learning (DML), and the hybrid of super learning (SL) and deep learning with support points sample splitting under double machine learning have produced a competing performance and results in terms of the best estimation compared to the two other methods. The first model was time efficient to estimate the causal inference compared to the third one. But the third model was better performing in terms of the estimation quality by producing the lowest MSE compared to the other two models. The results of this research are consistent with the recent development of machine learning. The support vector machine learning has been introduced in the previous century, and it looks like it is no longer showing efficiency and quality estimation with the recent emerging double machine learning. However, cutting-edge methods such as deep learning and super learner have shown superior performance in the estimation of the causal double machine learning target estimator, and efficiency in the time of computation

Big Data Analytics and Information Science for Business and Biomedical Applications

The analysis of Big Data in biomedical as well as business and financial research has drawn much attention from researchers worldwide. This book provides a platform for the deep discussion of state-of-the-art statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions are showcased

Proceedings of the 36th International Workshop Statistical Modelling July 18-22, 2022 - Trieste, Italy

The 36th International Workshop on Statistical Modelling (IWSM) is the first one held in presence after a two year hiatus due to the COVID-19 pandemic. This edition was quite lively, with 60 oral presentations and 53 posters, covering a vast variety of topics. As usual, the extended abstracts of the papers are collected in the IWSM proceedings, but unlike the previous workshops, this year the proceedings will be not printed on paper, but it is only online. The workshop proudly maintains its almost unique feature of scheduling one plenary session for the whole week. This choice has always contributed to the stimulating atmosphere of the conference, combined with its informal character, encouraging the exchange of ideas and cross-fertilization among different areas as a distinguished tradition of the workshop, student participation has been strongly encouraged. This IWSM edition is particularly successful in this respect, as testified by the large number of students included in the program