Label-aligned multi-task feature learning for multimodal classification of Alzheimer’s disease and mild cognitive impairment

Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer’s disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI

Towards generalizable machine learning models for computer-aided diagnosis in medicine

Hidden stratification represents a phenomenon in which a training dataset contains unlabeled (hidden) subsets of cases that may affect machine learning model performance. Machine learning models that ignore the hidden stratification phenomenon--despite promising overall performance measured as accuracy and sensitivity--often fail at predicting the low prevalence cases, but those cases remain important. In the medical domain, patients with diseases are often less common than healthy patients, and a misdiagnosis of a patient with a disease can have significant clinical impacts. Therefore, to build a robust and trustworthy CAD system and a reliable treatment effect prediction model, we cannot only pursue machine learning models with high overall accuracy, but we also need to discover any hidden stratification in the data and evaluate the proposing machine learning models with respect to both overall performance and the performance on certain subsets (groups) of the data, such as the ‘worst group’. In this study, I investigated three approaches for data stratification: a novel algorithmic deep learning (DL) approach that learns similarities among cases and two schema completion approaches that utilize domain expert knowledge. I further proposed an innovative way to integrate the discovered latent groups into the loss functions of DL models to allow for better model generalizability under the domain shift scenario caused by the data heterogeneity. My results on lung nodule Computed Tomography (CT) images and breast cancer histopathology images demonstrate that learning homogeneous groups within heterogeneous data significantly improves the performance of the computer-aided diagnosis (CAD) system, particularly for low-prevalence or worst-performing cases. This study emphasizes the importance of discovering and learning the latent stratification within the data, as it is a critical step towards building ML models that are generalizable and reliable. Ultimately, this discovery can have a profound impact on clinical decision-making, particularly for low-prevalence cases

Estimation of gender-specific connectional brain templates using joint multi-view cortical morphological network integration

The estimation of a connectional brain template (CBT) integrating a population of brain networks while capturing shared and differential connectional patterns across individuals remains unexplored in gender fingerprinting. This paper presents the first study to estimate gender-specific CBTs using multi-view cortical morphological networks (CMNs) estimated from conventional T1-weighted magnetic resonance imaging (MRI). Specifically, each CMN view is derived from a specific cortical attribute (e.g. thickness), encoded in a network quantifying the dissimilarity in morphology between pairs of cortical brain regions. To this aim, we propose Multi-View Clustering and Fusion Network (MVCF-Net), a novel multi-view network fusion method, which can jointly identify consistent and differential clusters of multi-view datasets in order to capture simultaneously similar and distinct connectional traits of samples. Our MVCF-Net method estimates a representative and well-centered CBTs for male and female populations, independently, to eventually identify their fingerprinting regions of interest (ROIs) in four main steps. First, we perform multi-view network clustering model based on manifold optimization which groups CMNs into shared and differential clusters while preserving their alignment across views. Second, for each view, we linearly fuse CMNs belonging to each cluster, producing local CBTs. Third, for each cluster, we non-linearly integrate the local CBTs across views, producing a cluster-specific CBT. Finally, by linearly fusing the cluster-specific centers we estimate a final CBT of the input population. MVCF-Net produced the most centered and representative CBTs for male and female populations and identified the most discriminative ROIs marking gender differences. The most two gender-discriminative ROIs involved the lateral occipital cortex and pars opercularis in the left hemisphere and the middle temporal gyrus and lingual gyrus in the right hemisphere.</p

Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

Quantifying cognitive and mortality outcomes in older patients following acute illness using epidemiological and machine learning approaches

Introduction: Cognitive and functional decompensation during acute illness in older people are poorly understood. It remains unclear how delirium, an acute confusional state reflective of cognitive decompensation, is contextualised by baseline premorbid cognition and relates to long-term adverse outcomes. High-dimensional machine learning offers a novel, feasible and enticing approach for stratifying acute illness in older people, improving treatment consistency while optimising future research design. Methods: Longitudinal associations were analysed from the Delirium and Population Health Informatics Cohort (DELPHIC) study, a prospective cohort ≥70 years resident in Camden, with cognitive and functional ascertainment at baseline and 2-year follow-up, and daily assessments during incident hospitalisation. Second, using routine clinical data from UCLH, I constructed an extreme gradient-boosted trees predicting 600-day mortality for unselected acute admissions of oldest-old patients with mechanistic inferences. Third, hierarchical agglomerative clustering was performed to demonstrate structure within DELPHIC participants, with predictive implications for survival and length of stay. Results: i. Delirium is associated with increased rates of cognitive decline and mortality risk, in a dose-dependent manner, with an interaction between baseline cognition and delirium exposure. Those with highest delirium exposure but also best premorbid cognition have the “most to lose”. ii. High-dimensional multimodal machine learning models can predict mortality in oldest-old populations with 0.874 accuracy. The anterior cingulate and angular gyri, and extracranial soft tissue, are the highest contributory intracranial and extracranial features respectively. iii. Clinically useful acute illness subtypes in older people can be described using longitudinal clinical, functional, and biochemical features. Conclusions: Interactions between baseline cognition and delirium exposure during acute illness in older patients result in divergent long-term adverse outcomes. Supervised machine learning can robustly predict mortality in in oldest-old patients, producing a valuable prognostication tool using routinely collected data, ready for clinical deployment. Preliminary findings suggest possible discernible subtypes within acute illness in older people

3D Deep Learning on Medical Images: A Review

The rapid advancements in machine learning, graphics processing technologies and availability of medical imaging data has led to a rapid increase in use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, give a brief mathematical description of 3D CNN and the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection, and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models, in general) and possible future trends in the field.Comment: 13 pages, 4 figures, 2 table

The impact of aerobic exercise on brain's white matter integrity in the Alzheimer's disease and the aging population

The brain is the most complex organ in the body. Currently, its complicated functionality has not been fully understood. However, in the last decades an exponential growth on research publications emerged thanks to the use of in-vivo brain imaging techniques. One of these techniques pioneered for medical use in the early 1970s was known as nuclear magnetic resonance imaging based (now called magnetic resonance imaging [MRI]). Nowadays, the advances of MRI technology not only allowed us to characterize volumetric changes in specific brain structures but now we could identify different patterns of activation (e.g. functional MRI) or changes in structural brain connectivity (e.g. diffusion MRI). One of the benefits of using these techniques is that we could investigate changes that occur in disease-specific cohorts such as in the case of Alzheimer’s disease (AD), a neurodegenerative disease that affects mainly older populations. This disease has been known for over a century and even though great advances in technology and pharmacology have occurred, currently there is no cure for the disease. Hence, in this work I decided to investigate whether aerobic exercise, an emerging alternative method to pharmacological treatments, might provide neuroprotective effects to slow down the evident brain deterioration of AD using novel in-vivo diffusion imaging techniques. Previous reports in animal and human studies have supported these exercise-related neuro-protective mechanisms. Concurrently in AD participants, increased brain volumes have been positively associated with higher cardiorespiratory fitness levels, a direct marker of sustained physical activity and increased exercise. Thus, the goal of this work is to investigate further whether exercise influences the brain using structural connectivity analyses and novel diffusion imaging techniques that go beyond volumetric characterization. The approach I chose to present this work combined two important aspects of the investigation. First, I introduced important concepts based on the neuro-scientific work in relation to Alzheimer’s diseases, in-vivo imaging, and exercise physiology (Chapter 1). Secondly, I tried to describe in simple mathematics the physics of this novel diffusion imaging technique (Chapter 2) and supported a tract-specific diffusion imaging processing methodology (Chapter 3 and 4). Consequently, the later chapters combined both aspects of this investigation in a manuscript format (Chapter 5-8). Finally, I summarized my findings, include recommendations for similar studies, described future work, and stated a final conclusion of this work (Chapter 9)

Deep Interpretability Methods for Neuroimaging

Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Nevertheless, the difficulty of reliable training on high-dimensional but small-sample datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this dissertation, we address these challenges by proposing a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. The developed model is pre-trainable and alleviates the need to collect an enormous amount of neuroimaging samples to achieve optimal training. We also provide a quantitative validation module, Retain and Retrain (RAR), that can objectively verify the higher predictability of the dynamics learned by the model. Results successfully demonstrate that the proposed framework enables learning the fMRI dynamics directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction. We also comprehensively reviewed deep interpretability literature in the neuroimaging domain. Our analysis reveals the ongoing trend of interpretability practices in neuroimaging studies and identifies the gaps that should be addressed for effective human-machine collaboration in this domain. This dissertation also proposed a post hoc interpretability method, Geometrically Guided Integrated Gradients (GGIG), that leverages geometric properties of the functional space as learned by a deep learning model. With extensive experiments and quantitative validation on MNIST and ImageNet datasets, we demonstrate that GGIG outperforms integrated gradients (IG), which is considered to be a popular interpretability method in the literature. As GGIG is able to identify the contours of the discriminative regions in the input space, GGIG may be useful in various medical imaging tasks where fine-grained localization as an explanation is beneficial

Abordagem CNN 2D estendida para o diagnóstico da doença de Alzheimer através de imagens de ressonância magnética estrutural

Orientadores: Leticia Rittner, Roberto de Alencar LotufoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A doença de Alzheimer (AD - Alzheimer's disease) é um tipo de demência que afeta milhões de pessoas em todo o mundo. Até o momento, não há cura para a doença e seu diagnóstico precoce tem sido uma tarefa desafiadora. As técnicas atuais para o seu diagnóstico têm explorado as informações estruturais da Imagem por Ressonância Magnética (MRI - Magnetic Resonance Imaging) em imagens ponderadas em T1. Entre essas técnicas, a rede neural convolucional (CNN - Convolutional Neural Network) é a mais promissora e tem sido usada com sucesso em imagens médicas para uma variedade de aplicações devido à sua capacidade de extração de características. Antes do grande sucesso do aprendizado profundo e das CNNs, os trabalhos que objetivavam classificar os diferentes estágios de AD exploraram abordagens clássicas de aprendizado de máquina e uma meticulosa extração de características, principalmente para classificar testes binários. Recentemente, alguns autores combinaram técnicas de aprendizagem profunda e pequenos subconjuntos do conjunto de dados públicos da Iniciativa de Neuroimagem da Doença de Alzheimer (ADNI - Alzheimer's Disease Neuroimaging Initiative) para prever um estágio inicial da doença explorando abordagens 3D CNN geralmente combinadas com arquiteturas de auto-codificador convolucional 3D. Outros também exploraram uma abordagem de CNN 3D combinando-a ou não com uma etapa de pré-processamento para a extração de características. No entanto, a maioria desses trabalhos focam apenas na classificação binária, sem resultados para AD, comprometimento cognitivo leve (MCI - Mild Cognitive Impairment) e classificação de sujeitos normais (NC - Normal Control). Nosso principal objetivo foi explorar abordagens de CNN 2D para a tarefa de classificação das 3 classes usando imagens de MRI ponderadas em T1. Como objetivo secundário, preenchemos algumas lacunas encontradas na literatura ao investigar o uso de arquiteturas CNN 2D para o nosso problema, uma vez que a maioria dos trabalhos explorou o aprendizado de máquina clássico ou abordagens CNN 3D. Nossa abordagem CNN 2D estendida explora as informações volumétricas dos dados de ressonância magnética, mantendo baixo custo computacional associado a uma abordagem 2D, quando comparados às abordagens 3D. Além disso, nosso resultado supera as outras estratégias para a classificação das 3 classes e comparando o desempenho de nosso modelo com os métodos tradicionais de aprendizado de máquina e 3D CNN. Também investigamos o papel de diferentes técnicas amplamente utilizadas em aplicações CNN, por exemplo, pré-processamento de dados, aumento de dados, transferência de aprendizado e adaptação de domínio para um conjunto de dados brasileiroAbstract: Alzheimer's disease (AD) is a type of dementia that affects millions of people around the world. To date, there is no cure for Alzheimer's and its early-diagnosis has been a challenging task. The current techniques for Alzheimer's disease diagnosis have explored the structural information of Magnetic Resonance Imaging (MRI) in T1-weighted images. Among these techniques, deep convolutional neural network (CNN) is the most promising one and has been successfully used in medical images for a variety of applications due to its ability to perform features extraction. Before the great success of deep learning and CNNs, the works that aimed to classify the different stages of AD explored classic machine learning approaches and a meticulous feature engineering extraction, mostly to classify binary tasks. Recently, some authors have combined deep learning techniques and small subsets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) public dataset, to predict an early-stage of AD exploring 3D CNN approaches usually combined with 3D convolutional autoencoder architectures. Others have also investigated a 3D CNN approach combining it or not with a pre-processing step for the extraction of features. However, the majority of these papers focus on binary classification only, with no results for Alzheimer's disease, Mild Cognitive Impairment (MCI), and Normal Control (NC) classification. Our primary goal was to explore 2D CNN approaches to tackle the 3-class classification using T1-weighted MRI. As a secondary goal, we filled some gaps we found in the literature by investigating the use of 2D CNN architectures to our problem, since most of the works either explored traditional machine learning or 3D CNN approaches. Our extended-2D CNN explores the MRI volumetric data information while maintaining the low computational costs associated with a 2D approach when compared to 3D-CNNs. Besides, our result overcomes the other strategies for the 3-class classification while analyzing the performance of our model with traditional machine-learning and 3D-CNN methods. We also investigated the role of different widely used techniques in CNN applications, for instance, data pre-processing, data augmentation, transfer-learning, and domain-adaptation to a Brazilian datasetMestradoEngenharia de ComputaçãoMestra em Engenharia Elétrica168468/2017-4  CNP