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

arXiv:2211.08559v2 Announce Type: cross
Abstract: Developing successful artificial intelligence systems in practice depends on both robust deep learning models and large, high-quality data. However, acquiring and labeling data can be 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 self-supervised …

abstract alzheimer's applications artificial artificial intelligence arxiv clinical cs.ai cs.cv cs.lg data deep learning disease domain however intelligence labeling modeling practice quality quality data robust self-supervised learning supervised learning systems type world