Introducing Vision Transformer for Alzheimer's Disease classification task with 3D input

Many high-performance classification models utilize complex CNN-based architectures for Alzheimer's Disease classification. We aim to investigate two relevant questions regarding classification of Alzheimer's Disease using MRI: "Do Vision Transformer-based models perform better than CNN-based models?" and "Is it possible to use a shallow 3D CNN-based model to obtain satisfying results?" To achieve these goals, we propose two models that can take in and process 3D MRI scans: Convolutional Voxel Vision Transformer (CVVT) architecture, and ConvNet3D-4, a shallow 4-block 3D CNN-based model. Our results indicate that the shallow 3D CNN-based models are sufficient to achieve good classification results for Alzheimer's Disease using MRI scans

Alzheimers Disease Diagnosis by Deep Learning Using MRI-Based Approaches

The most frequent kind of dementia of the nervous system, Alzheimer's disease, weakens several brain processes (such as memory) and eventually results in death. The clinical study uses magnetic resonance imaging to diagnose AD. Deep learning algorithms are capable of pattern recognition and feature extraction from the inputted raw data. As early diagnosis and stage detection are the most crucial elements in enhancing patient care and treatment outcomes, deep learning algorithms for MRI images have recently allowed for diagnosing a medical condition at the beginning stage and identifying particular symptoms of Alzheimer's disease. As a result, we aimed to analyze five specific studies focused on AD diagnosis using MRI-based deep learning algorithms between 2021 and 2023 in this study. To completely illustrate the differences between these techniques and comprehend how deep learning algorithms function, we attempted to explore selected approaches in depth

Large-scale neuroimaging in Alzheimer’s disease and normal aging

Large-scale neuroimaging data is becoming increasingly available, providing a rich data source with which to study neurological conditions. In this thesis, I demonstrate the utility of large-scale neuroimaging as it applies to Alzheimer’s disease (AD) and normal aging, using univariate parametric mapping, regional analysis, and advanced machine learning. Specifically, this thesis covers: 1) validation and extension of prior studies using large-scale datasets; 2) AD diagnosis and normal aging evaluation empowered by large-scale datasets and advanced deep learning algorithms; 3) enhancement of cerebral blood volume (CBV) fMRI utility with retrospective CBV-fMRI technique. First, I demonstrated the utility of large-scale datasets for validating and extending prior studies using univariate analytics. I presented a study localizing AD-vulnerable regions more reliably and with better anatomical resolution using data from more than 350 subjects. Following a similar approach, I investigated the structural characteristics of healthy APOE ε4 homozygous subjects screened from a large-scale community-based study. To study the neuroimaging signatures of normal aging, we performed a large-scale joint CBV-fMRI and structural MRI study covering age 20-70s, and a structural MRI study of normal aging covering the full age-span, with the elder group screened from a large-scale clinic-based study ensuring no evidence of AD using both longitudinal follow-up and cerebrospinal fluid (CSF) biomarkers evidences. Second, I performed deep learning neuroimaging studies for AD diagnosis and normal aging evaluation, and investigated the regionality associated with each task. I developed an AD diagnosis method using a 3D convolutional neural network model trained and evaluated on ~4,600 structural MRI scans and further investigated a series of novel regionality analyses. I further extensively studied the utility of the structural MRI summary measure derived from the deep learning model in prodromal AD detection. This study constitutes a general analytic framework, which was followed to evaluate normal aging by performing deep learning-based age estimation in cognitively normal population using more than 6,000 scans. The deep learning neuroimaging models classified AD and estimated age with high accuracy, and also revealed regional patterns conforming to neuropathophysiology. The deep learning derived MRI measure demonstrated potential clinical utility, outperforming other AD pathology measures and biomarkers. In addition, I explored the utility of deep learning on positron emission tomography (PET) data for AD diagnosis and regionality analyses, further demonstrating the broad utility and generalizability of the method. Finally, I introduced a technique enabling CBV generation retrospectively from clinical contrast-enhanced scans. The derivation of meaningful functional measures from such clinical scans is only possible through calibration to a reference, which was built from the largest collection of research CBV-fMRI scans from our lab. This method was validated in an epilepsy study and demonstrated the potential to enhance the utility of CBV-fMRI by enriching the CBV-fMRI dataset. This technique is also applicable to AD and normal aging studies, and potentially enables deep learning based analytic approaches applied on CBV-fMRI with similar pipelines used in structural MRI. Collectively, this thesis demonstrates how mechanistic and diagnostic information on brain disorders can be extracted from large-scale neuroimaging data, using both classical statistical methods and advanced machine learning

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

ADNet : diagnóstico assistido por computador para doença de Alzheimer usando rede neural convolucional 3D com cérebro inteiro

Orientadores: Anderson de Rezende Rocha, Marina WeilerDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Demência por doença de Alzheimer (DA) é uma síndrome clínica caracterizada por múltiplos problemas cognitivos, incluindo dificuldades na memória, funções executivas, linguagem e habilidades visuoespaciais. Sendo a forma mais comum de demência, essa doença mata mais do que câncer de mama e de próstata combinados, além de ser a sexta principal causa de morte nos Estados Unidos. A neuroimagem é uma das áreas de pesquisa mais promissoras para a detecção de biomarcadores estruturais da DA, onde uma técnica não invasiva é usada para capturar uma imagem digital do cérebro, a partir da qual especialistas extraem padrões e características da doença. Nesse contexto, os sistemas de diagnóstico assistido por computador (DAC) são abordagens que visam ajudar médicos e especialistas na interpretação de dados médicos, para fornecer diagnósticos aos pacientes. Em particular, redes neurais convolucionais (RNCs) são um tipo especial de rede neural artificial (RNA), que foram inspiradas em como o sistema visual funciona e, nesse sentido, têm sido cada vez mais utilizadas em tarefas de visão computacional, alcançando resultados impressionantes. Em nossa pesquisa, um dos principais objetivos foi utilizar o que há de mais avançado sobre aprendizagem profunda (por exemplo, RNC) para resolver o difícil problema de identificar biomarcadores estruturais da DA em imagem por ressonância magnética (IRM), considerando três grupos diferentes, ou seja, cognitivamente normal (CN), comprometimento cognitivo leve (CCL) e DA. Adaptamos redes convolucionais com dados fornecidos principalmente pela ADNI e avaliamos no desafio CADDementia, resultando em um cenário mais próximo das condições no mundo real, em que um sistema DAC é usado em um conjunto de dados diferente daquele usado no treinamento. Os principais desafios e contribuições da nossa pesquisa incluem a criação de um sistema de aprendizagem profunda que seja totalmente automático e comparativamente rápido, ao mesmo tempo em que apresenta resultados competitivos, sem usar qualquer conhecimento específico de domínio. Nomeamos nossa melhor arquitetura ADNet (Alzheimer's Disease Network) e nosso melhor método ADNet-DA (ADNet com adaptação de domínio), o qual superou a maioria das submissões no CADDementia, todas utilizando conhecimento prévio da doença, como regiões de interesse específicas do cérebro. A principal razão para não usar qualquer informação da doença em nosso sistema é fazer com que ele aprenda e extraia padrões relevantes de regiões importantes do cérebro automaticamente, que podem ser usados para apoiar os padrões atuais de diagnóstico e podem inclusive auxiliar em novas descobertas para diferentes ou novas doenças. Após explorar uma série de técnicas de visualização para interpretação de modelos, associada à inteligência artificial explicável (XAI), acreditamos que nosso método possa realmente ser empregado na prática médica. Ao diagnosticar pacientes, é possível que especialistas usem a ADNet para gerar uma diversidade de visualizações explicativas para uma determinada imagem, conforme ilustrado em nossa pesquisa, enquanto a ADNet-DA pode ajudar com o diagnóstico. Desta forma, os especialistas podem chegar a uma decisão mais informada e em menos tempoAbstract: Dementia by Alzheimer's disease (AD) is a clinical syndrome characterized by multiple cognitive problems, including difficulties in memory, executive functions, language and visuospatial skills. Being the most common form of dementia, this disease kills more than breast cancer and prostate cancer combined, and it is the sixth leading cause of death in the United States. Neuroimaging is one of the most promising areas of research for early detection of AD structural biomarkers, where a non-invasive technique is used to capture a digital image of the brain, from which specialists extract patterns and features of the disease. In this context, computer-aided diagnosis (CAD) systems are approaches that aim at assisting doctors and specialists in interpretation of medical data to provide diagnoses for patients. In particular, convolutional neural networks (CNNs) are a special kind of artificial neural network (ANN), which were inspired by how the visual system works, and, in this sense, have been increasingly used in computer vision tasks, achieving impressive results. In our research, one of the main goals was bringing to bear what is most advanced in deep learning research (e.g., CNN) to solve the difficult problem of identifying AD structural biomarkers in magnetic resonance imaging (MRI), considering three different groups, namely, cognitively normal (CN), mild cognitive impairment (MCI), and AD. We tailored convolutional networks with data primarily provided by ADNI, and evaluated them on the CADDementia challenge, thus resulting in a scenario very close to the real-world conditions, in which a CAD system is used on a dataset differently from the one used for training. The main challenges and contributions of our research include devising a deep learning system that is both completely automatic and comparatively fast, while also presenting competitive results, without using any domain specific knowledge. We named our best architecture ADNet (Alzheimer's Disease Network), and our best method ADNet-DA (ADNet with domain adaption), which outperformed most of the CADDementia submissions, all of them using prior knowledge from the disease, such as specific regions of interest of the brain. The main reason for not using any information from the disease in our system is to make it automatically learn and extract relevant patterns from important regions of the brain, which can be used to support current diagnosis standards, and may even assist in new discoveries for different or new diseases. After exploring a number of visualization techniques for model interpretability, associated with explainable artificial intelligence (XAI), we believe that our method can be actually employed in medical practice. While diagnosing patients, it is possible for specialists to use ADNet to generate a diversity of explanatory visualizations for a given image, as illustrated in our research, while ADNet-DA can assist with the diagnosis. This way, specialists can come up with a more informed decision and in less timeMestradoCiência da ComputaçãoMestre em Ciência da Computaçã