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.

翻译：基础模型正经历着普及热潮。Segment Anything模型（SAM）宣称能够分割广泛类别的物体，但需要规模空前的监督训练。我们将SAM的性能（与临床金标准对比）及资源消耗（标注时间、计算量），与一种针对特定模态的无标签自监督学习（SSL）方法，在100例心脏超声的25项测量上进行了比较。SAM表现不佳，且需要显著更多的标注与计算资源，其效率低于SSL。