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What is Optimizing Prompts for Lung Segmentation about?

This paper proposes a method called SAMPOT for optimizing prompts for foundation segmentation models. SAMPOT iteratively evolves the location of an initial user-provided prompt to improve segmentation performance on a given task, such as lung segmentation from chest X-rays. It does this by maximizing an unsupervised task-based scoring function to find an optimal prompt location, without requiring ground truth labels. The method is demonstrated to improve segmentation results for around 75% of test images co

Author

brilliantalex470

Language

EN