  This paper introduces the DeepATLAS foundational model for localization tasks
in the domain of high-dimensional biomedical data. Upon convergence of the
proposed self-supervised objective, a pretrained model maps an input to an
anatomically-consistent embedding from which any point or set of points (e.g.,
boxes or segmentations) may be identified in a one-shot or few-shot approach.
As a representative benchmark, a DeepATLAS model pretrained on a comprehensive
cohort of 51,000+ unlabeled 3D computed tomography exams yields high one-shot
segmentation performance on over 50 anatomic structures across four different
external test sets, either matching or exceeding the performance of a standard
supervised learning model. Further improvements in accuracy can be achieved by
adding a small amount of labeled data using either a semisupervised or more
conventional fine-tuning strategy.
