2025
- Temporal representation learning for real-time ultrasound analysis
Stebler Y., Sutter T. M., Ozkan E., and Vogt J. E., | arXiv preprint - From slices to structures: Unsupervised 3D reconstruction of female pelvic anatomy from freehand transvaginal ultrasound
Krähenmann M., Tascon-Morales S., Laumer F., Vogt J. E., and Ozkan, E. | arXiv preprint - Predicting pulmonary hypertension in newborns: A multi-view VAE approach
Erlacher L., Ruipérez-Campillo S., Michel H., Wellmann S., Sutter T.M, Ozkan E., Vogt, J.E. | AI for Children: Healthcare
2024
- Multi-domain improves classification in out-of-distribution and data-limited scenarios for medical image analysis
Ozkan E. and Boix X. | Scientific Reports - Deep learning based prediction of pulmonary hypertension in newborns using echocardiograms
Ragnarsdottir H., Ozkan E., Michel H., Chin-Cheong K., Manduchi L., Wellmann S., Vogt J.E. | International Journal of Computer Vision - Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis
Marcinkevičs R., Wolfertstetter P. R., Klimiene U., Chin-Cheong K., Ozkan E., et al. | Medical Image Analysis - Large language models for wearable data analysis and interpretation
Böhi S. and Gashi S. | Proceedings of the 2nd Tiny Papers Track at ICLR 2024
2023
- M(otion)-mode based prediction of ejection fraction using echocardiograms
Ozkan E., Sutter T. M., Hu Y., Balzer S. and Vogt J. E. | Proceedings of the German Conference on Pattern Recognition - Introduction to machine learning for physicians: A survival guide for data deluge
Vogt J. E., Ozkan E. and Marcinkevičs R. | Digital Medicine
2022
- Debiasing deep chest X-ray classifiers using intra- and post-processing methods
Marcinkevičs R., Ozkan E. and Vogt J. E. | Proceedings of the 7th Machine Learning for Healthcare Conference
2020
- Displacement estimation methods for speed-of-sound imaging in pulse-echo
Rau R., Ozkan E., Ozturkler B. M., Gastli L. and Goksel O. | Proceedings of the IEEE International Ultrasonics Symposium
