March 14, 2024, 4:42 a.m. | Benjamin D. Killeen, Liam J. Wang, Han Zhang, Mehran Armand, Russell H. Taylor, Greg Osgood, Mathias Unberath

cs.LG updates on

arXiv:2403.08059v1 Announce Type: cross
Abstract: Automated X-ray image segmentation would accelerate research and development in diagnostic and interventional precision medicine. Prior efforts have contributed task-specific models capable of solving specific image analysis problems, but the utility of these models is restricted to their particular task domain, and expanding to broader use requires additional data, labels, and retraining efforts. Recently, foundation models (FMs) -- machine learning models trained on large amounts of highly variable data thus enabling broad applicability -- have …

abstract analysis arxiv automated contributed cs.lg development diagnostic domain foundation foundation model image language medicine precision precision medicine prior ray research research and development segmentation type utility x-ray

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