Early identification of mild cognitive impairment using incomplete random forest-robust support vector machine and FDG-PET imaging

Alzheimer’s disease (AD) is the most common type of dementia and will be an increasing health problem in society as the population ages. Mild cognitive impairment (MCI) is considered to be a prodromal stage of AD. The ability to identify subjects with MCI will be increasingly important as disease modifying therapies for AD are developed. We propose a semi-supervised learning method based on robust optimization for the identification of MCI from [18F]Fluorodeoxyglucose PET scans. We extracted three groups of spatial features from the cortical and subcortical regions of each FDG-PET image volume. We measured the statistical uncertainty related to these spatial features via transformation using an incomplete random forest and formulated the MCI identification problem under a robust optimization framework. We compared our approach to other state-of-the-art methods in different learning schemas. Our method outperformed the other techniques in the ability to separate MCI from normal controls

Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson\u27s disease

Parkinson\u27s disease (PD) is a common, progressive, and currently incurable neurodegenerative movement disorder. The diagnosis of PD is challenging, especially in the differential diagnosis of parkinsonism and in early PD detection. Due to the advantages of machine learning such as learning complex data patterns and making inferences for individuals, machine-learning techniques have been increasingly applied to the diagnosis of PD, and have shown some promising results. Machine-learning-based imaging applications have made it possible to help differentiate parkinsonism and detect PD at early stages automatically in a number of neuroimaging studies. Comparative studies have shown that machine-learning-based SPECT image analysis applications in PD have outperformed conventional semi-quantitative analysis in detecting PD-associated dopaminergic degeneration, performed comparably well as experts\u27 visual inspection, and helped improve PD diagnostic accuracy of radiologists. Using combined multi-modal (imaging and clinical) data in these applications may further enhance PD diagnosis and early detection. To integrate machine-learning-based diagnostic applications into clinical systems, further validation and optimization of these applications are needed to make them accurate and reliable. It is anticipated that machine-learning techniques will further help improve differential diagnosis of parkinsonism and early detection of PD, which may reduce the error rate of PD diagnosis and help detect PD at pre-motor stage to make it possible for early treatments (e.g., neuroprotective treatment) to slow down PD progression, prevent severe motor symptoms from emerging, and relieve patients from suffering

An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment

One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain function associated with gait impairment could be crucial in better understanding PD motor progression, thus advancing the development of more effective and personalized therapeutics. In this work, we present an explainable, geometric, weighted-graph attention neural network (xGW-GAT) to identify functional networks predictive of the progression of gait difficulties in individuals with PD. xGW-GAT predicts the multi-class gait impairment on the MDS Unified PD Rating Scale (MDS-UPDRS). Our computational- and data-efficient model represents functional connectomes as symmetric positive definite (SPD) matrices on a Riemannian manifold to explicitly encode pairwise interactions of entire connectomes, based on which we learn an attention mask yielding individual- and group-level explainability. Applied to our resting-state functional MRI (rs-fMRI) dataset of individuals with PD, xGW-GAT identifies functional connectivity patterns associated with gait impairment in PD and offers interpretable explanations of functional subnetworks associated with motor impairment. Our model successfully outperforms several existing methods while simultaneously revealing clinically-relevant connectivity patterns. The source code is available at https://github.com/favour-nerrise/xGW-GAT .Comment: Accepted by the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023). MICCAI Student-Author Registration (STAR) Award. 11 pages, 2 figures, 1 table, appendix. Source Code: https://github.com/favour-nerrise/xGW-GA

Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization

Automatic speech recognition (ASR) has recently become an important challenge when using deep learning (DL). It requires large-scale training datasets and high computational and storage resources. Moreover, DL techniques and machine learning (ML) approaches in general, hypothesize that training and testing data come from the same domain, with the same input feature space and data distribution characteristics. This assumption, however, is not applicable in some real-world artificial intelligence (AI) applications. Moreover, there are situations where gathering real data is challenging, expensive, or rarely occurring, which can not meet the data requirements of DL models. deep transfer learning (DTL) has been introduced to overcome these issues, which helps develop high-performing models using real datasets that are small or slightly different but related to the training data. This paper presents a comprehensive survey of DTL-based ASR frameworks to shed light on the latest developments and helps academics and professionals understand current challenges. Specifically, after presenting the DTL background, a well-designed taxonomy is adopted to inform the state-of-the-art. A critical analysis is then conducted to identify the limitations and advantages of each framework. Moving on, a comparative study is introduced to highlight the current challenges before deriving opportunities for future research

MACHINE LEARNING BASED ANALYSIS AND COMPUTER AIDED CLASSIFICATION OF NEUROPSYCHIATRIC DISORDERS USING NEUROIMAGING

Machine learning (ML) based analysis of neuroimages in neuropsychiatry context are advancing the understanding of neurobiological profiles and the pathological bases of neuropsychiatric disorders. Computational analysis and investigations on features derived from structural magnetic resonance imaging (sMRI) of the brain are used to quantify morphological or anatomical characteristics of the different regions of the brain that have role in several distinct brain functions. This helps in the realization of anatomical underpinnings of those disorders that cause brain atrophy. Structural neuroimaging data acquired from schizophrenia (SCZ), bipolar disorder (BD) patients and people who experienced psychosis for the first time, are used for the experiments presented in this thesis. The cerebral cortex (i.e., gray matter) of the brain is one of the most studied anatomical part using 'cortical-average-thickness' distribution feature in the literature. This helps in the realization of the anatomical underpinning of those mental illnesses that cause brain atrophy. To this regard, based on statistical background, 'cortical-skewness' feature, a novel digital imaging-derived neuroanatomical biomarker that could potentially assist in the differentiation of healthy control (HC) and patient groups is proposed and tested in this thesis. The core theme of machine intelligence relies in extracting and learning patterns of input data from experience. Classification is one of the task. In a basic set up, ML algorithms are trained using exemplary multivariate data features and its associated class labels, so that they could be able to create models and do predictive classification and other tasks. Considering the conundrum nature of psychiatric disorders, researchers in the field, could benefit from ML based analysis of complex brain patterns. Out of many, one task is computer aided classification (CAC). This is achieved by training the algorithms, these complex brain patterns and their corresponding diagnostic statistics manual (DSM) based clinical gold standard labels. Indeed, in the literature, supervised learning methods such as support vector machines (SVM) which follow inductive learning strategy are widely exploited and achieved interesting results. Observing this and due to the fact that the most widely available relevant anatomical features of the cortex such as thickness and volume values, could not be considered satisfactory features because of the heterogeneous nature of the human brain anatomy due to differences in age, gender etc., a contextual similarity based learning is proposed. This learning uses a transductive learning mechanism (i.e, learn a specific function for the problem at hand) instead of learning a general function to solve a specific problem. Based on this, it is adopted, a formulation of a semi supervised graph transduction (label propagation) algorithm based on the notions of game theory, where the consistent labeling is represented with Nash equilibrium, to tackle the problem of learning from neuroimages with subtle microscopic difference among different clinical groups. However, since such kind of algorithms heavily rely on the graph structure of the extracted features, we extended the classification procedure by introducing a pre-training phase based on a distance metric learning strategy with the aim of enhancing the contextual similarity of the images by providing a 'must belong in the same class' and 'must not belong in the same class' constraint from the available training data. This would result to increase intra-class similarity and decrease inter-class similarity. The proposed classification pipeline is used for searching anatomical biomarkers. With the goal of identifying potential neuroanatomical markers of a psychiatric disorder, it is aimed to develop a feature selection strategy taking into consideration the widely exploited cortical thickness and the proposed skewness feature, with the objective of searching a combination of features from all cortical regions of the brain that could maximize the possible differentiation among the different clinical groups Considering Research Domain Criteria (RDoC) framework developed by National Institute of Mental Health (NIMH) with the aim of developing biologically valid perspective of mental disorders by integrating multimodal sources, clinical interview scores and neuroimaging data are used with ML methods to tackle the challenging problem of differential classification of BD vs. SCZ. Finally, as deep learning methods are emerging with remarkable results in several application domains, we adopted this class of methods especially convolutional neural networks (CNNs) with a 3D approach, to extract volumetric neuroanatomical markers. CAC of first episode psychosis (FEP) is performed by exploiting the 3D complex spatial structure of the brain to identify key regions of the brain associated with the pathophysiology of FEP. Testing of individualized predictions with big dataset of 855 structural scans to identify possible markers of the disease is performed

Machine Learning Methods for Structural Brain MRIs: Applications for Alzheimer’s Disease and Autism Spectrum Disorder

This thesis deals with the development of novel machine learning applications to automatically detect brain disorders based on magnetic resonance imaging (MRI) data, with a particular focus on Alzheimer’s disease and the autism spectrum disorder. Machine learning approaches are used extensively in neuroimaging studies of brain disorders to investigate abnormalities in various brain regions. However, there are many technical challenges in the analysis of neuroimaging data, for example, high dimensionality, the limited amount of data, and high variance in that data due to many confounding factors. These limitations make the development of appropriate computational approaches more challenging. To deal with these existing challenges, we target multiple machine learning approaches, including supervised and semi-supervised learning, domain adaptation, and dimensionality reduction methods.In the current study, we aim to construct effective biomarkers with sufficient sensitivity and specificity that can help physicians better understand the diseases and make improved diagnoses or treatment choices. The main contributions are 1) development of a novel biomarker for predicting Alzheimer’s disease in mild cognitive impairment patients by integrating structural MRI data and neuropsychological test results and 2) the development of a new computational approach for predicting disease severity in autistic patients in agglomerative data by automatically combining structural information obtained from different brain regions.In addition, we investigate various data-driven feature selection and classification methods for whole brain, voxel-based classification analysis of structural MRI and the use of semi-supervised learning approaches to predict Alzheimer’s disease. We also analyze the relationship between disease-related structural changes and cognitive states of patients with Alzheimer’s disease.The positive results of this effort provide insights into how to construct better biomarkers based on multisource data analysis of patient and healthy cohorts that may enable early diagnosis of brain disorders, detection of brain abnormalities and understanding effective processing in patient and healthy groups. Further, the methodologies and basic principles presented in this thesis are not only suited to the studied cases, but also are applicable to other similar problems

Deep learning for automobile predictive maintenance under Industry 4.0

Industry 4.0 refers to the fourth industrial revolution, which has boosted the development of the world. An important target of Industry 4.0 is to maximize the asset uptime so to improve productivity and reduce the production and maintenance cost. The emerging techniques such as artificial intelligence (AI), industrial Internet of things (IIoT) and cyber-physical system (CPS) have accelerated the development of data-orientated application such as predictive maintenance (PdM). Maintenance is a big concern for an automobile fleet management company. An accurate maintenance prediction can be helpful to avoid critical failure and avoid further loss. Deep learning is a type of prevailing machine learning algorithm which has been widely used in big data analytics. However, how to establish a maintenance prediction model based on historical maintenance data using deep learning has not been investigated. Moreover, it is worthwhile to study how to build a prediction model when the labelled data is insufficient. Furthermore, surrounding factors which may impact automobile lifecycle have not been concerned in the state-of-the-art. Hence, this thesis will focus on how to pave the way for automobile PdM under Industry 4.0. This research is structured according to four themes. Firstly, different from the conventional PdM research that only focuses on modelling based on sensor data or historical maintenance data, a framework for automobile PdM based on multi-source data is proposed. The proposed framework aims at automobile TBF modelling, prediction, and decision support based on the multi-source data. There are five layers designed in this framework, which are data collection, cloud data transmission and storage, data mapping, pre-processing and integration, deep learning for automobile TBF modelling, and decision support for PdM. This framework covers the entire knowledge discovery process from data collection to decision support. Secondly, one of the purposes of this thesis is to establish a Time-Between-Failure (TBF) prediction model through a data-driven approach. An accurate automobile TBF iv Abstract prediction can bring tangible benefits to a fleet management company. Different from the existing studies that adopted sensor data for failure time prediction, a new approach called Cox proportional hazard deep learning (CoxPHDL) is proposed based on the historical maintenance data for TBF modelling and prediction. CoxPHDL is able to tackle the data sparsity and data censoring issues that are common in the analysis of historical maintenance data. Firstly, an autoencoder is adopted to convert the nominal data into a robust representation. Secondly, a Cox PHM is researched to estimate the TBF of the censored data. A long-short-term memory (LSTM) network is then established to train the TBF prediction model based on the pre-processed maintenance data. Experimental results have demonstrated the merits of the proposed approach. Thirdly, a large amount of labelled data is one of the critical factors to the satisfactory algorithm performance of deep learning. However, labelled data is expensive to collect in the real world. In order to build a TBF prediction model using deep learning when the labelled data is limited, a new semi-supervised learning algorithm called deep learning embedded semi-supervised learning (DLeSSL) is proposed. Based on DLeSSL, unlabelled data can be estimated using a semi-supervised learning approach that has a deep learning technique embedded so to expand the labelled dataset. Results derived using the proposed method reveal that deep learning (DLeSSL based) outperforms the benchmarking algorithms when the labelled data is limited. In addition, different from existing studies, the effect on algorithm performance due to the size of labelled data and unlabelled data is reported to offer insights for the deployment of DLeSSL. Finally, automobile lifecycle can be impacted by surrounding factors such as weather, traffic, and terrain. The data contains these factors can be collected and processed via geographical information system (GIS). To introduce these GIS data into automobile TBF modelling, an integrated approach is proposed. This is the first time that the surrounding factors are considered in the study of automobile TBF modelling. Meanwhile, in order to build a TBF prediction model based on multi-source data, a new deep learning architecture called merged-LSTM (M-LSTM) network is designed. Abstract v Experimental results derived using the proposed approach and M-LSTM network reveal the impacts of the GIS factors. This thesis aims to research automobile PdM using deep learning, which provides a feasibility study for achieving Industry 4.0. As such, it offers great potential as a route to achieving a more profitable, efficient, and sustainable fleet management

Medical Image Analytics (Radiomics) with Machine/Deeping Learning for Outcome Modeling in Radiation Oncology

Image-based quantitative analysis (radiomics) has gained great attention recently. Radiomics possesses promising potentials to be applied in the clinical practice of radiotherapy and to provide personalized healthcare for cancer patients. However, there are several challenges along the way that this thesis will attempt to address. Specifically, this thesis focuses on the investigation of repeatability and reproducibility of radiomics features, the development of new machine/deep learning models, and combining these for robust outcomes modeling and their applications in radiotherapy. Radiomics features suffer from robustness issues when applied to outcome modeling problems, especially in head and neck computed tomography (CT) images. These images tend to contain streak artifacts due to patients’ dental implants. To investigate the influence of artifacts for radiomics modeling performance, we firstly developed an automatic artifact detection algorithm using gradient-based hand-crafted features. Then, comparing the radiomics models trained on ‘clean’ and ‘contaminated’ datasets. The second project focused on using hand-crafted radiomics features and conventional machine learning methods for the prediction of overall response and progression-free survival for Y90 treated liver cancer patients. By identifying robust features and embedding prior knowledge in the engineered radiomics features and using bootstrapped LASSO to select robust features, we trained imaging and dose based models for the desired clinical endpoints, highlighting the complementary nature of this information in Y90 outcomes prediction. Combining hand-crafted and machine learnt features can take advantage of both expert domain knowledge and advanced data-driven approaches (e.g., deep learning). Thus, we proposed a new variational autoencoder network framework that modeled radiomics features, clinical factors, and raw CT images for the prediction of intrahepatic recurrence-free and overall survival for hepatocellular carcinoma (HCC) patients in this third project. The proposed approach was compared with widely used Cox proportional hazard model for survival analysis. Our proposed methods achieved significant improvement in terms of the prediction using the c-index metric highlighting the value of advanced modeling techniques in learning from limited and heterogeneous information in actuarial prediction of outcomes. Advances in stereotactic radiation therapy (SBRT) has led to excellent local tumor control with limited toxicities for HCC patients, but intrahepatic recurrence still remains prevalent. As an extension of the third project, we not only hope to predict the time to intrahepatic recurrence, but also the location where the tumor might recur. This will be clinically beneficial for better intervention and optimizing decision making during the process of radiotherapy treatment planning. To address this challenging task, firstly, we proposed an unsupervised registration neural network to register atlas CT to patient simulation CT and obtain the liver’s Couinaud segments for the entire patient cohort. Secondly, a new attention convolutional neural network has been applied to utilize multimodality images (CT, MR and 3D dose distribution) for the prediction of high-risk segments. The results showed much improved efficiency for obtaining segments compared with conventional registration methods and the prediction performance showed promising accuracy for anticipating the recurrence location as well. Overall, this thesis contributed new methods and techniques to improve the utilization of radiomics for personalized radiotherapy. These contributions included new algorithm for detecting artifacts, a joint model of dose with image heterogeneity, combining hand-crafted features with machine learnt features for actuarial radiomics modeling, and a novel approach for predicting location of treatment failure.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163092/1/liswei_1.pd

Multi-Modal Magnetic Resonance Imaging Predicts Regional Amyloid Burden in the Brain

Alzheimer’s disease (AD) is the most common cause of dementia and identifying early markers of this disease is important for prevention and treatment strategies. Amyloid- β (Aβ) protein deposition is one of the earliest detectable pathological changes in AD. But in-vivo detection of Aβ using positron emission tomography (PET) is hampered by high cost and limited geographical accessibility. These factors can become limiting when PET is used to screen large numbers of subjects into prevention trials when only a minority are expected to be amyloid-positive. Structural MRI is advantageous; as it is non-invasive, relatively inexpensive and more accessible. Thus it could be widely used in large studies, even when frequent or repetitive imaging is necessary. We used a machine learning, pattern recognition, approach using intensity-based features from individual and combination of MR modalities (T1 weighted, T2 weighted, T2 fluid attenuated inversion recovery [FLAIR], susceptibility weighted imaging) to predict voxel-level amyloid in the brain. The MR- Aβ relation was learned within each subject and generalized across subjects using subject–specific features (demographic, clinical, and summary MR features). When compared to other modalities, combination of T1-weighted, T2-weighted FLAIR, and SWI performed best in predicting the amyloid status as positive or negative. A combination of T2-weighted and SWI imaging performed the best in predicting change in amyloid over two timepoints. Overall, our results show feasibility of amyloid prediction by MRI and its potential use as an amyloid-screening tool