Download PDFOpen PDF in browserFrom Unlabeled Data to Clinical Applications: Foundation Models in Medical Imaging4 pages•Published: January 5, 2026AbstractThe performance of deep learning algorithms is highly dependent on the quantity and diversity of the available training data. However, obtaining sufficiently large datasets represents a significant challenge, particularly in the field of medical imaging. This study underscores the potential of self-supervised training strategies in the development of deep learning models for medical imaging tasks. It is demonstrated that workflows can be significantly optimized by incorporating the feature content of a large collection of medical X-ray images from intraoperative C-arm scans into a so-called foundation model. This approach facilitates the efficient adaptation to a variety of concrete applications by fine-tuning a small task-specific head network on top of the pre-trained foundation model, thereby reducing both computational demands and training time.Keyphrases: artificial intelligence, foundation models, machine learning, medical imaging In: Joshua William Giles and Aziliz Guezou-Philippe (editors). Proceedings of The 25th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 8, pages 152-155.
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