Schaefer, Raphael and Nicke, Till and Hoefener, Henning and Lange, Annkristin and Merhof, Dorit and Feuerhake, Friedrich and Schulz, Volkmar and Lotz, Johannes and Kiessling, Fabian (2024) Overcoming data scarcity in biomedical imaging with a foundational multi-task model. NATURE COMPUTATIONAL SCIENCE, 4 (7). ISSN , 2662-8457
Full text not available from this repository. (Request a copy)Abstract
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. UMedPT, a foundational model for biomedical imaging, has been trained on a variety of medical tasks with different types of label. It has achieved high performance with less training data in various clinical applications.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | DEEP NEURAL-NETWORKS; |
| Subjects: | 000 Computer science, information & general works > 004 Computer science |
| Divisions: | Informatics and Data Science > Department Computational Life Science > Chair of Image Analysis and Computer Vision (Prof. Dr.-Ing. Dorit Merhof) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 17 Nov 2025 13:36 |
| Last Modified: | 17 Nov 2025 13:36 |
| URI: | https://pred.uni-regensburg.de/id/eprint/64885 |
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