Publications
Here you find a list of our selected current publications. You can find all publications here.
- C. I. Bercea, B. Wiestler, D. Rueckert and J. A. Schnabel
Evaluating Normative Representation Learning in Generative AI for Robust Anomaly Detection in Brain Imaging
Nature Communications, 16(1), 1624 (2025)
DOI: https://doi.org/10.1038/s41467-025-56321-y
- R.Holland, O. Leingang, H. Bogunović, S. Riedl, L. Fritsche, T. Prevost, H.P.N. Scholl, U. Schmidt-Erfurth, S. Sivaprasad, A.J. Lotery, D. Rueckert and M.J. Menten
Metadata-enhanced contrastive learning from retinal optical coherence tomography images
Medical Image Analysis, 97, 103296 (2024)
DOI: https://doi.org/10.1016/j.media.2024.103296
- A. Ziller, T. T. Mueller, S. Stieger, L. F. Feiner, J. Brandt, R. Braren, D. Rueckert and G. Kaissis
Reconciling privacy and accuracy in AI for medical imaging
Nature Machine Intelligence, 6(7), 764-774 (2024)
DOI: https://doi.org/10.1038/s42256-024-00858-y
- P. Hager, F. Jungmann, K. Bhagat, I. Hubrecht, M. Knauer, J. Vielhauer, R. Braren, M. Makowski, G. Kaisis and D. Rueckert
Evaluating and mitigating limitations of large language models in clinical decision making
Nature Medicine 30, 2613–2622 (2024)
DOI: https://doi.org/10.1038/s41591-024-03097-1
- I. Lagogiannis, F. Meissen, G. Kaissis and D. Rueckert
Unsupervised Pathology Detection: A Deep Dive Into the State of the Art
IEEE transactions on medical imaging, 43(1): 241-252 (2024)
DOI: https://doi.org/10.1109/tmi.2023.3298093
- R. Raab, A. Küderle, A. Zakreuskaya, A. D. Stern, J. Klucken, G. Kaissis, D. Rueckert, S. Boll, R. Eils, H. Wagener and B. M. Eskofier
Federated electronic health records for the European Health
Lancet Digital Health, 5: e840–47 (2023)
DOI: https://doi.org/10.1016/s2589-7500%2823%2900156-5
- E. Al-jibury, J. W. D. King, Y. Guo, B. Lenhard, A. G. Fisher, M. Merkenschlager and D. Rueckert
A deep learning method for replicate-based analysis of chromosome conformation contacts using Siamese neural networks
Nature Communications 14(5007) (2023)
DOI: https://doi.org/10.1038/s41467-023-40547-9
- T. Tanida, P. Müller, G. Kaissis and D. Rueckert
Interactive and Explainable Region-guided Radiology Report Generation
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7433-7442 (2023).
DOI: https://doi.org/10.1109/cvpr52729.2023.00718
- Q. Meng, W. Bai, D. P. O’Regan and D. Rueckert
DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning
IEEE transactions on medical imaging, 43(4): 1489-1500 (2024)
DOI: https://doi.org/10.1109/TMI.2023.3340118
- A. Marcus, P. Bentley and D. Rueckert
Concurrent ischemic lesion age estimation and segmentation of CT brain using a Transformer-based network
IEEE Transactions on Medical Imaging, 42(12):3464-3473 (2023)
DOI: https://doi.org/10.1109/TMI.2023.3287361
- W. Huang, H. B. Li, J. Pan, G. Cruz, D. Rueckert and K. Hammernik
Neural Implicit k-Space for Binning-Free Non-Cartesian Cardiac MR Imaging
Information Processing in Medical Imaging (IPMI), 548-560, 2023
DOI: https://doi.org/10.1007/978-3-031-34048-2_42
- P. Hager, M. J. Menten and D. Rueckert
Best of Both Worlds: Multimodal Contrastive Learning With Tabular and Imaging Data
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 23924-23935, 2023.
DOI: https://doi.org/10.48550/arXiv.2303.14080
- C. Ouyang, C. Chen, S. Li, Z. Li, C. Qin, W. Bai and D. Rueckert
Causality-inspired Single-source Domain Generalization for Medical Image Segmentation
IEEE Transactions on Medical Imaging, 42(4):1095-1106 (2023)
DOI: https://doi.org/10.1109/TMI.2022.3224067
- C. Chen, C. Qin, C. Ouyang, Z. Li, S. Wang, H. Qiu, L. Chen, G. Tarroni, W. Bai and D. Rueckert
Enhancing MR image segmentation with realistic adversarial data augmentation
Medical Image Analysis, 82 (2022)
DOI: https://doi.org/10.1016/j.media.2022.102597
- C. Qin, S. Wang, C. Chen, W. Bai and D. Rueckert
Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior
Medical Image Analysis, 83 (2023)
DOI: https://doi.org/10.1016/j.media.2022.102682
- C. Ouyang, C. Biffi, C. Chen, T. Kart, H. Qiu and D. Rueckert
Self-supervised Learning for Few-shot Medical Image Segmentation
IEEE Transactions on Medical Imaging, 41(7):1837-1848 (2022)
DOI: https://doi.org/10.1109/TMI.2022.3150682
- A. D. Edwards, D. Rueckert and S. M Smith
The Developing Human Connectome Project Neonatal Data Release
Frontiers in Neuroscience, 16 (2022)
DOI: https://doi.org/10.3389/fnins.2022.886772
- T. T. Mueller, J. C. Paetzold, C. Prabhakar, D. Usynin, D. Rueckert and G. Kaissis
Private Graph Neural Networks for Whole-Graph Classification
IEEE transactions on medical imaging, 43(4): 1489-1500 (2022)
DOI: https://doi.org/10.1109/TPAMI.2022.3228315
- D. Usynin, A. Ziller, M. Makowski, R. Braren, D. Rueckert, B. Glocker and G. Kaissis
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning
Nature Machine Intelligence (3), 749–758 (2021)
DOI: https://doi.org/10.1038/s42256-021-00390-3
- Q. Dou, T. Y. So, M. Jiang, Q. Liu, V. Vardhanabhuti, G. Kaissis, Z. Li, W. Si, H. H. C. Lee, K. Yu, Z. Feng, L. Dong, E. Burian, F. Jungmann, R. Braren, M. Makowski, B. Kainz, D. Rueckert, B. Glocker, S. C. H. Yu and P. A. Heng
Federated deep learning for detecting COVID-19 lung abnormalities in CT: A privacy-preserving multinational validation study
NPJ Digital Medicine 60(4) (2021)
DOI: https://doi.org/10.1038/s41746-021-00431-6
