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.

翻译：本文介绍了用于高维生物医学数据定位任务的DeepATLAS基础模型。在提出的自监督目标收敛后，预训练模型将输入映射到解剖学一致的嵌入空间，从中可基于一次性或少样本方法识别任意点或点集（如边界框或分割区域）。作为代表性基准测试，基于超过51,000例未标注三维CT检查数据预训练的DeepATLAS模型，在四个不同外部测试集上对50余个解剖结构实现了高精度一次性分割，其性能达到或超越标准监督学习模型。通过结合少量标注数据（采用半监督或常规微调策略），可进一步提升模型准确性。