Fôlego ADNet : Computer-Aided Diagnosis for Alzheimer ’ s Disease Using Whole-Brain 3 D Convolutional Neural Network ADNet : Diagnóstico Assistido por Computador para Doença de Alzheimer

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 time.