Y. Stebler, T. M. Sutter, E. Ozkan, and J. E. Vogt, “Temporal representation learning for real-time ultrasound analysis,” arXiv preprint arXiv:2509.01433, 2025.
M. Krähenmann, S. Tascon-Morales, F. Laumer, J. E. Vogt, and E. Ozkan, “From slices to structures: Unsupervised 3D reconstruction of female pelvic anatomy from freehand transvaginal ultrasound,” arXiv preprint arXiv:2508.14552, 2025.
Lucas Erlacher, Samuel Ruipérez-Campillo, Holger Michel, Sven Wellmann, Thomas M. Sutter, Ece Ozkan, Julia E. Vogt, “Predicting pulmonary hypertension in newborns: A multi-view VAE approach,” in AI for Children: Healthcare, Psychology, Education, 2025.
E. Ozkan and X. Boix, “Multi-domain improves classification in out-of-distribution and data-limited scenarios for medical image analysis,” Scientific Reports, vol. 14, no. 1, p. 24412, 2024.
Hanna Ragnarsdottir*, Ece Ozkan*, Holger Michel*, Kieran Chin-Cheong, Laura Manduchi, Sven Wellmann*, Julia E Vogt*, “Deep learning based prediction of pulmonary hypertension in newborns using echocardiograms,” International Journal of Computer Vision, pp. 1–18, 2024.
R. Marcinkevičs*, P. R. Wolfertstetter*, U. Klimiene*, K. Chin-Cheong, E. Ozkan, et al., “Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis,” Medical Image Analysis, vol. 91, p. 103042, 2024.
S. Böhi and S. Gashi, “Large language models for wearable data analysis and interpretation.” in Proceedings of the 2nd Tiny Papers Track at ICLR 2024 (Tiny Papers @ ICLR 2024), OpenReview, doi:10.3929/ethz-b-000661839, 2024.
E. Ozkan*, T. M. Sutter*, Y. Hu, S. Balzer, and J. E. Vogt, “M(otion)-mode based prediction of ejection fraction using echocardiograms,” in Proceedings of the German Conference on Pattern Recognition (GCPR), pp. 307–320, 2023.
R. Marcinkevičs, P. R. Wolfertstetter, U. Klimiene, E. Özkan Elsen, et al., “Regensburg pediatric appendicitis dataset,” 2023. (Dataset publication — venue not specified)
J. E. Vogt, E. Ozkan, and R. Marcinkevičs, “Introduction to machine learning for physicians: A survival guide for data deluge,” in Digital Medicine, pp. 3–34, 2023.
R. Marcinkevičs, E. Ozkan, and J. E. Vogt, “Debiasing deep chest X-ray classifiers using intra- and post-processing methods,” in Proceedings of the 7th Machine Learning for Healthcare Conference (MLHC), 2022.
R. Rau, E. Ozkan, B. M. Ozturkler, L. Gastli, and O. Goksel, “Displacement estimation methods for speed-of-sound imaging in pulse-echo,” in Proceedings of the IEEE International Ultrasonics Symposium (IUS), pp. 1–4, 2020.
