Victor Hugo Rocha de Oliveira

Bruno Machado Pacheco, Victor Hugo Rocha de Oliveira, Augusto Braga Fernandes Antunes et al.

Brain age prediction using neuroimaging data has shown great potential as an indicator of overall brain health and successful aging, as well as a disease biomarker. Deep learning models have been established as reliable and efficient brain age estimators, being trained to predict the chronological age of healthy subjects. In this paper, we investigate the impact of a pre-training step on deep learning models for brain age prediction. More precisely, instead of the common approach of pre-training on natural imaging classification, we propose pre-training the models on brain-related tasks, which led to state-of-the-art results in our experiments on ADNI data. Furthermore, we validate the resulting brain age biomarker on images of patients with mild cognitive impairment and Alzheimer's disease. Interestingly, our results indicate that better-performing deep learning models in terms of brain age prediction on healthy patients do not result in more reliable biomarkers.

Victor H. R. Oliveira, Augusto Antunes, Alexandre S. Soares et al.

In this work, deep learning techniques for brain age prediction from magnetic resonance images are investigated, aiming to assist in the identification of biomarkers of the natural aging process. The identification of biomarkers is useful for detecting an early-stage neurodegenerative process, as well as for predicting age-related or non-age-related cognitive decline. Two techniques are implemented and compared in this work: a 3D Convolutional Neural Network applied to the volumetric image and a 2D Convolutional Neural Network applied to slices from the axial plane, with subsequent fusion of individual predictions. The best result was obtained by the 2D model, which achieved a mean absolute error of 3.83 years. -- Neste trabalho são investigadas técnicas de aprendizado profundo para a predição da idade cerebral a partir de imagens de ressonância magnética, visando auxiliar na identificação de biomarcadores do processo natural de envelhecimento. A identificação de biomarcadores é útil para a detecção de um processo neurodegenerativo em estágio inicial, além de possibilitar prever um declínio cognitivo relacionado ou não à idade. Duas técnicas são implementadas e comparadas neste trabalho: uma Rede Neural Convolucional 3D aplicada na imagem volumétrica e uma Rede Neural Convolucional 2D aplicada a fatias do plano axial, com posterior fusão das predições individuais. O melhor resultado foi obtido pelo modelo 2D, que alcançou um erro médio absoluto de 3.83 anos.