Daniel Rückert

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Daniel Rückert

Dr.

 https://orcid.org/0000-0002-5683-5889
1994 …2025

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  • Classification, Regression and Segmentation Directly from K-Space in Cardiac MRI

    Li, R., Pan, J., Zhu, Y., Ni, J. & Rueckert, D., 2025, Machine Learning in Medical Imaging - 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Proceedings. Xu, X., Cui, Z., Sun, K., Rekik, I. & Ouyang, X. (eds.). Springer Science and Business Media Deutschland GmbH, p. 31-41 11 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 15241 LNCS).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • Fair and Private CT Contrast Agent Detection

    Kaess, P., Ziller, A., Mantz, L., Rueckert, D., Fintelmann, F. J. & Kaissis, G., 2025, Ethics and Fairness in Medical Imaging - 2nd International Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with MICCAI 2024, Proceedings. Puyol-Antón, E., King, A. P., Zamzmi, G., Feragen, A., Petersen, E., Cheplygina, V., Ganz-Benjaminsen, M., Ferrante, E., Glocker, B., Rekik, I., Baxter, J. S. H. & Eagleson, R. (eds.). Springer Science and Business Media Deutschland GmbH, p. 34-45 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 15198 LNCS).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

  • 3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression From Longitudinal OCTs

    Emre, T., Chakravarty, A., Rivail, A., Lachinov, D., Leingang, O., Riedl, S., Mai, J., Scholl, H. P. N., Sivaprasad, S., Rueckert, D., Lotery, A., Schmidt-Erfurth, U. & Bogunovic, H., 2024, In: IEEE Transactions on Medical Imaging. 43, 9, p. 3200-3210 11 p.

    Research output: Contribution to journal › Article › peer-review

    Open Access
    1 Scopus citations

  • A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression

    Bintsi, K. M., Mueller, T. T., Starck, S., Baltatzis, V., Hammers, A. & Rueckert, D., 2024, Graphs in Biomedical Image Analysis, and Overlapped Cell on Tissue Dataset for Histopathology - 5th MICCAI Workshop, GRAIL 2023 and 1st MICCAI Challenge, OCELOT 2023, Held in Conjunction with MICCAI 2023, Proceedings. Ahmadi, S.-A. & Pereira, S. (eds.). Springer Science and Business Media Deutschland GmbH, p. 64-73 10 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 14373 LNCS).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

    Open Access

  • A Learnable Prior Improves Inverse Tumor Growth Modeling

    Weidner, J., Ezhov, I., Balcerak, M., Metz, M. C., Litvinov, S., Kaltenbach, S., Feiner, L., Lux, L., Kofler, F., Lipkova, J., Latz, J., Rueckert, D., Menze, B. & Wiestler, B., 2024, (Accepted/In press) In: IEEE Transactions on Medical Imaging.

    Research output: Contribution to journal › Article › peer-review

    Open Access
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