2019/04/11 by Qingyu Zhao, Ehsan Adeli, Zhao, Qingyu +7 · 3 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Cell Image Analysis Techniques #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.1904.05948
openalex publication_date 2019/04/11 · openalex created_date 2019/04/25 · openalex updated_date 2026/07/28
While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified probabilistic model for learning the latent space of imaging data and performing supervised regression. Based on recent advances in learning disentangled representations, the novel generative process explicitly models the conditional distribution of latent representations with respect to the regression target variable. Performing a variational inference procedure on this model leads to joint regularization between the VAE and a neural-network regressor. In predicting the age of 245 subjects from their structural Magnetic Resonance (MR) images, our model is more accurate than state-of-the-art methods when applied to either region-of-interest (ROI) measurements or raw 3D volume images. More importantly, unlike simple feed-forward neural-networks, disentanglement of age in latent representations allows for intuitive interpretation of the structural developmental patterns of the human brain.