This article discusses the opportunities, applications and future directions of large-scale pre-trained models, i.e., foundation models, for analyzing medical images. Medical foundation models have immense potential in solving a wide range of downstream tasks, as they can help to accelerate the development of accurate and robust models, reduce the large amounts of required labeled data, preserve the privacy and confidentiality of patient data. Specifically, we illustrate the "spectrum" of medical foundation models, ranging from general vision models, modality-specific models, to organ/task-specific models, highlighting their challenges, opportunities and applications. We also discuss how foundation models can be leveraged in downstream medical tasks to enhance the accuracy and efficiency of medical image analysis, leading to more precise diagnosis and treatment decisions.

翻译：本文探讨了大规模预训练模型（即基础模型）在医学图像分析中的机遇、应用及未来方向。医学基础模型在解决广泛下游任务方面具有巨大潜力，能够加速开发准确且鲁棒的模型，减少所需的大量标注数据，并保护患者数据的隐私与机密性。具体而言，我们阐释了医学基础模型的“光谱”范围，涵盖通用视觉模型、模态特异性模型以及器官/任务特异性模型，重点分析了它们的挑战、机遇与应用。此外，我们还讨论了如何在下游医学任务中利用基础模型提升医学图像分析的准确性与效率，从而助力更精准的诊断与治疗决策。