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.

提出了一种多任务学习策略，使用 bioMedical PreTrained 模型（UMedPT）在包括断层、显微和 X 光图像的多任务数据库上进行训练，其在预训练数据库相关任务上具有卓越性能，且对于领域外任务仅需少量训练数据便可保持优越性能，对于跨中心可转移性，UMedPT 提取的影像特征成为新的标准。