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 specialized 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 labeling strategies such as classification, segmentation and object detection. The UMedPT foundational model outperformed ImageNet pretraining and previous state-of-the-art models. For classification 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 only 50% of the original training data. In an external independent validation, imaging features extracted using UMedPT proved to set a new standard for cross-center transferability.
CITATION STYLE
Schäfer, R., Nicke, T., Höfener, H., Lange, A., Merhof, D., Feuerhake, F., … Kiessling, F. (2024). Overcoming data scarcity in biomedical imaging with a foundational multi-task model. Nature Computational Science, 4(7), 495–509. https://doi.org/10.1038/s43588-024-00662-z
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