From Unlabeled Data to Clinical Applications: Foundation Models in Medical Imaging

Abstract

The 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.

Ähnliche Arbeiten