Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more heterogeneous datasets common in biomedical imaging. Here, we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a Universal bioMedical PreTrained model (UMedPT) on a multi-task database including tomographic, microscopic, and X-ray images, with various labelling strategies such as classification, segmentation, and object detection. The UMedPT foundational model outperformed ImageNet pretraining and the previous state-of-the-art models. For tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required not more than 50% of the original training data. In an external independent validation imaging features extracted using UMedPT proved to be a new standard for cross-center transferability.

翻译：基础模型在大规模预训练下已在非医学领域取得显著成功。然而，训练这些模型通常需要大量、全面的数据集，这与生物医学成像中常见的小规模且异质性强的数据集形成对比。在此，我们提出了一种多任务学习策略，该策略将训练任务数量与内存需求解耦。我们基于包含断层扫描、显微成像和X射线图像的多任务数据库训练了一个通用生物医学预训练模型（UMedPT），该数据库涵盖分类、分割和目标检测等多种标注策略。UMedPT基础模型在性能上优于ImageNet预训练及先前最先进模型。对于与预训练数据库相关的任务，该模型仅需1%的原始训练数据且无需微调即可保持性能；对于域外任务，所需原始训练数据不超过50%。在外部独立验证中，使用UMedPT提取的成像特征被证明是跨中心可迁移性的新标准。