Impact of dataset size on fine‐tuning foundation models for neuroanatomic segmentation: Testing the foundation model hypothesis

SAM and MedSAM did not outperform a standard UNet model in segmentation tasks, even in extremely limited training data settings, contrary to the foundation model hypothesis, suggesting that foundation models do not necessarily yield superior fine-tuned performance compared to standard segmentation models in the low data setting. Instead, the potential benefit of foundation models will depend on the characteristics of the task at hand and the behavior and capacity of the specific foundation model in question. Thus, it will be essential to benchmark against standard supervised deep learning methods for each distinct application to demonstrate the added value of using a foundation model.