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DeepATLAS: One-Shot Localization for Biomedical Data

arXiv 2402.09587 ↗First posted 14 Feb 2024cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 20 of its 48 references that are in the map. First posted on arXiv 14 Feb 2024.

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References · 20

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8 more
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