Foundation models in deep learning are characterized by a single large-scale model trained on vast amounts of data serving as the foundation for various downstream tasks. Foundation models are generally trained using self-supervised learning and excel in reducing the demand for training samples in downstream applications. This is especially important in medicine, where large labelled datasets are often scarce. Here, we developed a foundation model for cancer imaging biomarker discovery by training a convolutional encoder through self-supervised learning using a comprehensive dataset of 11,467 radiographic lesions. The foundation model was evaluated in distinct and clinically relevant applications of cancer imaging-based biomarkers. We found that it facilitated better and more efficient learning of imaging biomarkers and yielded task-specific models that significantly outperformed conventional supervised and other state-of-the-art pretrained implementations on downstream tasks, especially when training dataset sizes were very limited. Furthermore, the foundation model was more stable to input variations and showed strong associations with underlying biology. Our results demonstrate the tremendous potential of foundation models in discovering new imaging biomarkers that may extend to other clinical use cases and can accelerate the widespread translation of imaging biomarkers into clinical settings.

深度学习中的基础模型的特点是在大量数据上训练的单个大型模型，作为各种下游任务的基础。基础模型通常使用自监督学习进行训练，并且擅长减少下游应用程序对训练样本的需求。这在医学中尤其重要，因为大型标记数据集通常很稀缺。在这里，我们使用 11,467 个放射线病变的综合数据集通过自监督学习训练卷积编码器，开发了癌症成像生物标志物发现的基础模型。该基础模型在基于癌症成像的生物标志物的独特且临床相关的应用中进行了评估。我们发现它促进了更好、更有效地学习成像生物标记物，并产生了特定于任务的模型，该模型在下游任务上显着优于传统的监督和其他最先进的预训练实现，特别是当训练数据集大小非常有限时。此外，基础模型对输入变化更加稳定，并显示出与基础生物学的强烈关联。我们的结果证明了基础模型在发现新的成像生物标志物方面的巨大潜力，这些新的成像生物标志物可以扩展到其他临床用例，并可以加速成像生物标志物在临床环境中的广泛转化。