# Are foundation models efficient for medical image segmentation?

arXiv: https://arxiv.org/abs/2311.04847 · HTML: https://mlsanity.com/paper/2311.04847/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 8 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets](https://mlsanity.com/paper/2304.09324/) (2023)
- [Label-free segmentation from cardiac ultrasound using self-supervised learning](https://mlsanity.com/paper/2210.04979/) (2022)

## Built on by (most cited first, 2 of 2)

- [The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)](https://mlsanity.com/paper/2403.02558/) (2024)
- [Generative deep learning for foundational video translation in ultrasound](https://mlsanity.com/paper/2511.03255/) (2025)

## Abstract (arXiv)

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.