| Federated disentangled representation learning for unsupervised brain anomaly detection CI Bercea, B Wiestler, D Rueckert, S Albarqouni Nature Machine Intelligence 4 (8), 685-695, 2022 | 58 | 2022 |
| Feddis: Disentangled federated learning for unsupervised brain pathology segmentation CI Bercea, B Wiestler, D Rueckert, S Albarqouni arXiv preprint arXiv:2103.03705, 2021 | 45 | 2021 |
| Mask, stitch, and re-sample: Enhancing robustness and generalizability in anomaly detection through automatic diffusion models CI Bercea, M Neumayr, D Rueckert, JA Schnabel arXiv preprint arXiv:2305.19643, 2023 | 29 | 2023 |
| Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection CI Bercea, B Wiestler, D Rueckert, JA Schnabel International Conference on Medical Image Computing and Computer-Assisted …, 2023 | 22 | 2023 |
| Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening CI Bercea, B Wiestler, D Rueckert, JA Schnabel Medical Imaging with Deep Learning, 2023 | 20 | 2023 |
| What do AEs learn? Challenging common assumptions in unsupervised anomaly detection CI Bercea, D Rueckert, JA Schnabel International Conference on Medical Image Computing and Computer-Assisted …, 2023 | 18* | 2023 |
| Denoising diffusion models for 3d healthy brain tissue inpainting A Durrer, J Wolleb, F Bieder, P Friedrich, L Melie-Garcia, ... MICCAI Workshop on Deep Generative Models, 87-97, 2024 | 8 | 2024 |
| Confidence-aware levenberg-marquardt optimization for joint motion estimation and super-resolution C Bercea, A Maier, T Köhler 2016 IEEE International Conference on Image Processing (ICIP), 1136-1140, 2016 | 6 | 2016 |
| Multispectral 3D Masked Autoencoders for Anomaly Detection in Non-Contrast Enhanced Breast MRI DM Lang, E Schwartz, CI Bercea, R Giryes, JA Schnabel MICCAI Workshop on Cancer Prevention through Early Detection, 55-67, 2023 | 4* | 2023 |
| SHAMANN: Shared memory augmented neural networks CI Bercea, O Pauly, A Maier, FC Ghesu Information Processing in Medical Imaging: 26th International Conference …, 2019 | 4 | 2019 |
| Diffusion models with implicit guidance for medical anomaly detection CI Bercea, B Wiestler, D Rueckert, JA Schnabel International Conference on Medical Image Computing and Computer-Assisted …, 2024 | 3 | 2024 |
| Bias in Unsupervised Anomaly Detection in Brain MRI CI Bercea, E Puyol-Antón, B Wiestler, D Rueckert, JA Schnabel, AP King Workshop on Clinical Image-Based Procedures, 122-131, 2023 | 3 | 2023 |
| Multi-Image Visual Question Answering for Unsupervised Anomaly Detection J Li, CI Bercea, P Müller, L Felsner, S Kim, D Rueckert, B Wiestler, ... arXiv preprint arXiv:2404.07622, 2024 | 1 | 2024 |
| Towards Universal Unsupervised Anomaly Detection in Medical Imaging CI Bercea, B Wiestler, D Rueckert, JA Schnabel arXiv preprint arXiv:2401.10637, 2024 | 1 | 2024 |
| Tracking of multiple objects in cooperation with multiple neural networks CI Bercea US Patent App. 17/696,024, 2022 | 1 | 2022 |
| SPA: Shape-Prior Variational Autoencoders for Unsupervised Brain Pathology Segmentation CI Bercea, B Wiestler, D Rueckert, S Albarqouni | 1 | 2022 |
| Denoising Diffusion Models for Anomaly Localization in Medical Images CI Bercea, PC Cattin, JA Schnabel, J Wolleb arXiv preprint arXiv:2410.23834, 2024 | | 2024 |
| MedEdit: Counterfactual Diffusion-Based Image Editing on Brain MRI MB Alaya, DM Lang, B Wiestler, JA Schnabel, CI Bercea International Workshop on Simulation and Synthesis in Medical Imaging, 167-176, 2024 | | 2024 |
| Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound H Mykula, L Gasser, S Lobmaier, JA Schnabel, V Zimmer, CI Bercea International Workshop on Advances in Simplifying Medical Ultrasound, 220-230, 2024 | | 2024 |
| Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit Representations S Ambekar, JA Schnabel, CI Bercea arXiv preprint arXiv:2410.03306, 2024 | | 2024 |
Julia A SchnabelTechnical University of Munich, Helmholtz Munich and King's College LondonVerified email at tum.de
