Deep Learning in Medical Image Analysis

The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements

Unsupervised Manifold Learning using High-order Morphological Brain Networks derived from T1-w MRI for Autism Diagnosis

Brain disorders, such as Autism Spectrum Disorder (ASD), alter brain functional (from fMRI) and structural (from diffusion MRI) connectivities at multiple levels and in varying degrees. While unraveling such alterations have been the focus of a large number of studies, morphological brain connectivity has been out of the research scope. In particular, shape-to-shape relationships across brain regions of interest (ROIs) were rarely investigated. As such, the use of networks based on morphological brain data in neurological disorder diagnosis, while leveraging the advent of machine learning, could complement our knowledge on brain wiring alterations in unprecedented ways. In this paper, we use conventional T1-weighted MRI to define morphological brain networks (MBNs), each quantifying shape relationship between different cortical regions for a specific cortical attribute at both low-order and high-order levels. While typical brain connectomes investigate the relationship between two ROIs, we propose high-order MBN which better captures brain complex interactions by modeling the morphological relationship between pairs of ROIs. For ASD identification, we present a connectomic manifold learning framework, which learns multiple kernels to estimate a similarity measure between ASD and normal controls (NC) connectional features, to perform dimensionality reduction for clustering ASD and NC subjects. We benchmark our ASD identification method against both supervised and unsupervised state-of-the-art methods, while depicting the most discriminative high- and low-order relationships between morphological regions in the left and right hemispheres

Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist the specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We conclude by suggesting future approaches to detecting ASDs using AI techniques and MRI neuroimaging

The role of MRI in diagnosing autism: a machine learning perspective.

There is approximately 1 in every 44 children in the United States suffers from autism spectrum disorder (ASD), a disorder characterized by social and behavioral impairments. Communication difficulties, interpersonal difficulties, and behavioral difficulties are the top common symptoms. Even though symptoms can begin as early as infancy, it may take multiple visits to a pediatric specialist before an accurate diagnosis can be made. In addition, the diagnosis can be subjective, and different specialists may give different scores. There is a growing body of research suggesting differences in brain development and/or environmental and/or genetic factors contribute to autism development, but scientists have yet to identify exactly the pathology of this disorder. ASD can currently be diagnosed by a set of diagnostic evaluations, regarded as the gold standard, such as the Autism Diagnostic Observation Schedule (ADOS) or the Autism Diagnostic Interview-Revised (ADI-R). A team of qualified clinicians is needed for performing the behavioral and communication tests as well as clinical history information, hence a considerable amount of time, effort, and subjective judgment is involved in using these gold-standard diagnostic instruments. In addition to standard observational assessment, recent advancements in neuroimaging and machine learning suggest a rapid and objective alternative, using brain imaging. An investigation of the employment of different imaging modalities, namely Diffusion Tensor Imaging (DTI), and resting state functional MRI (rs-fMRI) for autism diagnosis is presented in this comprehensive work. A detailed study of the implementation of feature engineering tools to find discriminant insights from different brain imaging modalities, including the use of novel feature representations, and the use of a machine learning framework to assist in the accurate classification of autistic individuals is introduced in this dissertation. Based on three large publicly available datasets, this extensive research highlights different decisions along the pipeline and their impact on diagnostic accuracy. It also identifies potentially impacted brain regions that contribute to an autism diagnosis. Achieving high global state-of-the-art cross-validated accuracy confirms the benefits of feature representation and feature engineering in extracting useful information, as well as the potential benefits of utilizing neuroimaging in the diagnosis of autism. This should enable an early, automated, and more objective personalized diagnosis

Uncertainty Estimation, Explanation and Reduction with Insufficient Data

Human beings have been juggling making smart decisions under uncertainties, where we manage to trade off between swift actions and collecting sufficient evidence. It is naturally expected that a generalized artificial intelligence (GAI) to navigate through uncertainties meanwhile predicting precisely. In this thesis, we aim to propose strategies that underpin machine learning with uncertainties from three perspectives: uncertainty estimation, explanation and reduction. Estimation quantifies the variability in the model inputs and outputs. It can endow us to evaluate the model predictive confidence. Explanation provides a tool to interpret the mechanism of uncertainties and to pinpoint the potentials for uncertainty reduction, which focuses on stabilizing model training, especially when the data is insufficient. We hope that this thesis can motivate related studies on quantifying predictive uncertainties in deep learning. It also aims to raise awareness for other stakeholders in the fields of smart transportation and automated medical diagnosis where data insufficiency induces high uncertainty. The thesis is dissected into the following sections: Introduction. we justify the necessity to investigate AI uncertainties and clarify the challenges existed in the latest studies, followed by our research objective. Literature review. We break down the the review of the state-of-the-art methods into uncertainty estimation, explanation and reduction. We make comparisons with the related fields encompassing meta learning, anomaly detection, continual learning as well. Uncertainty estimation. We introduce a variational framework, neural process that approximates Gaussian processes to handle uncertainty estimation. Two variants from the neural process families are proposed to enhance neural processes with scalability and continual learning. Uncertainty explanation. We inspect the functional distribution of neural processes to discover the global and local factors that affect the degree of predictive uncertainties. Uncertainty reduction. We validate the proposed uncertainty framework on two scenarios: urban irregular behaviour detection and neurological disorder diagnosis, where the intrinsic data insufficiency undermines the performance of existing deep learning models. Conclusion. We provide promising directions for future works and conclude the thesis

Convolutional Autoencoder for Studying Dynamic Functional Brain Connectivity in Resting-State Functional MRI

Brain is the most complex organ in human body. Understanding how different regions of the brain function and interact with one another is a challenging task. One of the most important topics in the study of the brain is the functional brain connectivity, which is defined as the correlations, each between a pair of the activation signals from the different regions of the brain. Study of the functional connectivity in the human brain provides new insights into the understanding of the healthy and diseased brains and their differences. Functional magnetic resonance imaging (fMRI) is an imaging technique that allows researchers to study the brain activity and functional connectivity. While many researchers have focused on static functional connectivity in the resting-state fMRI to study the functions of the brain, dynamic functional connectivity has received more attention recently for such a study, since it provides more detailed information about the brain functions. Within the literature for studying the dynamic brain connectivity, k-means clustering has been applied to the connectivity matrices in order to find the functional connectivity patterns. However, it is known that the k-means clustering technique is not suitable for applying it to high dimensional data such as the functional brain connectivity matrices. In this thesis, in order to overcome this problem, we propose a deep learning-based convolutional autoencoder to obtain latent representations of the connectivity matrices prior to applying to them the k-means clustering. Use of the convolutional autoencoder, not only reduces the dimension of the connectivity matrices, but also provides a more semantic representation of these matrices. It is shown that the proposed method of clustering that consists of the use of the autoencoder followed by k-means clustering results in improving the clustering of the connectivity matrices, and consequently, to a better capturing of the functional connectivity patterns. In order to show the effectiveness of the proposed clustering method, synthetic connectivity matrices for patterns, with their classes known, are generated. The proposed method is then first applied to these syntactically generated connectivity matrices and the resulting patterns are compared with that obtained by applying k-means clustering technique to the synthetic connectivity matrices. It is shown that the proposed method classifies the various patterns more accurately. The proposed method is then used to study the dynamic functional brain connectivity by applying it to real fMRI data captured from a group of healthy subjects and another group of subjects affected by schizophrenia. For this purpose, after preprocessing the raw fMRI data for each subject in these two groups, the group independent component analysis (ICA) is applied in order to decompose the fMRI data into statistically independent components (map of the entire brain) and their corresponding time-courses. Each independent component corresponds to a specific region of the brain. The connectivity matrix whose elements corresponds to the correlation between the time-courses within a segment of the time-courses enclosed inside a sliding window is then obtained. Next, the proposed clustering method is used to cluster all the connectivity matrices, each corresponding to one segment, into a finite number of functional connectivity patterns (states). A two-sample t-test is then performed on each state in order to determine each pair of the regions in the group of the healthy control subjects for which weather or not the correlation value is significantly different from that of the corresponding pair of the regions in the group of schizophrenia patients. It is observed through this test that there are indeed pairs of the brain regions where significant differences do exist between the two groups. It is also seen that such a difference between the two groups is even more pronounced in the visual network of the brain. Finally, in this thesis, a study is undertaken for the evaluation of the dwell time, which is defined to be the duration for a functional connectivity pattern to remain in one state before switching to another state. It is shown through this study that the dwell time for the healthy group to stay in the state with more connectivity is longer than that for the group with schizophrenia. On the other hand, the dwell time for the group with schizophrenia to stay in the state with less connectivity is longer than that for the healthy group