Are foundation models efficient for medical image segmentation?

Danielle Ferreira, Rima Arnaout
11/8/2023
eess.IVcs.CV

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Abstract

Foundation models are experiencing a surge in popularity. The Segment Anything model (SAM) asserts an ability to segment a wide spectrum of objects but required supervised training at unprecedented scale. We compared SAM's performance (against clinical ground truth) and resources (labeling time, compute) to a modality-specific, label-free self-supervised learning (SSL) method on 25 measurements for 100 cardiac ultrasounds. SAM performed poorly and required significantly more labeling and computing resources, demonstrating worse efficiency than SSL.

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Cite this paper

@article{ferreira2023foundation,
  title  = {Are foundation models efficient for medical image segmentation?},
  author = {Danielle Ferreira and Rima Arnaout},
  year   = {2023},
  eprint = {2311.04847},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.04847v1}
}

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