Abstract: DINO Adapted to X-ray (DAX)

Scheuplein J, Rohleder M, Maier A, Kreher BW (2026)

Publication Type: Conference contribution

Publication year: 2026

Journal

Informatik aktuell Gesellschaft für Informatik (GI)

Publisher: IEEE

City/Town: New York City

Pages Range: 217-217

Conference Proceedings Title: Bildverarbeitung für die Medizin 2026German

Event location: Lübeck DE

ISBN: 9783658510992

DOI: 10.1007/978-3-658-51100-5_44

Abstract

Intraoperative X-ray imaging represents a key technology for guiding orthopaedic interventions. Recent advancements in deep learning have enabled automated image analysis in this field, thereby streamlining clinical workflows and enhancing patient outcomes. However, many existing approaches depend on task-specific models and are constrained by the limited availability of annotated data. In contrast, selfsupervised foundation models have exhibited remarkable potential to learn robust feature representations without label annotations. In this work, we introduce DINO adapted to X-ray (DAX), a novel framework that adapts DINO for training foundational feature extraction backbones tailored to intraoperative X-ray imaging. Our approach involves pre-training on a dataset comprising over 632,000 image samples, which surpasses other publicly available datasets in both size and feature diversity. To validate the successful incorporation of relevant domain knowledge into our DAX models, we conduct an extensive evaluation of all backbones on three distinct downstream tasks and demonstrate that small head networks can be trained on top of our frozen foundation models to successfully solve applications regarding (1) body region classification, (2) metal implant segmentation, and (3) screw object detection. The results of our study underscore the potential of the DAX framework to facilitate the development of robust, scalable, and clinically impactful deep learning solutions for intraoperative X-ray image analysis. Source code and model checkpoints are publicly available at https://github.com/JoshuaScheuplein/DAX [1].

Authors with CRIS profile

Joshua Scheuplein Lehrstuhl für Informatik 5 (Mustererkennung) Maximilian Rohleder Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) (LME) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens Healthineers (Germany) DE Germany (DE)

How to cite

APA:

Scheuplein, J., Rohleder, M., Maier, A., & Kreher, B.W. (2026). Abstract: DINO Adapted to X-ray (DAX). In Bildverarbeitung für die Medizin 2026German (pp. 217-217). Lübeck, DE: New York City: IEEE.

MLA:

Scheuplein, Joshua, et al. "Abstract: DINO Adapted to X-ray (DAX)." Proceedings of the German Conference on Medical Image Computing, Lübeck New York City: IEEE, 2026. 217-217.

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