Cross-Domain Self-Supervised Deep Learning for Robust Alzheimer's Disease Progression Modeling

Abstract

Developing successful artificial intelligence systems in practice depends both on robust deep learning models as well as large high quality data. Acquiring and labeling data can become prohibitively expensive and time-consuming in many real-world applications such as clinical disease models. Self-supervised learning has demonstrated great potential in increasing model accuracy and robustness in small data regimes. In addition, many clinical imaging and disease modeling applications rely heavily on regression of continuous quantities. However, the applicability of selfsupervised learning for these medical-imaging regression tasks has not been extensively studied. In this study, we develop a cross-domain self-supervised learning approach for disease prognostic modeling as a regression problem using 3D images as input. We demonstrate that self-supervised pre-training can improve the prediction of Alzheimer's Disease progression from brain MRI. We also show that pretraining on extended (but not labeled) brain MRI data outperforms pre-training on natural images. We further observe that the highest performance is achieved when both natural images and extended brain-MRI data are used for pre-training.