nnu-net: Self-adapting framework for u-net-based medical image segmentation F Isensee, J Petersen, A Klein, D Zimmerer, PF Jaeger, S Kohl, ... arXiv preprint arXiv:1809.10486, 2018 | 619 | 2018 |
Exploiting the potential of unlabeled endoscopic video data with self-supervised learning T Ross, D Zimmerer, A Vemuri, F Isensee, M Wiesenfarth, S Bodenstedt, ... International journal of computer assisted radiology and surgery 13, 925-933, 2018 | 112 | 2018 |
Context-encoding Variational Autoencoder for Unsupervised Anomaly Detection--Short Paper D Zimmerer, S Kohl, J Petersen, F Isensee, K Maier-Hein Medical Imaging with Deep Learning (MIDL) 2019, arXiv: 1907.12258, 2019 | 94* | 2019 |
Unsupervised anomaly localization using variational auto-encoders D Zimmerer, F Isensee, J Petersen, S Kohl, K Maier-Hein Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 92 | 2019 |
batchgenerators—a python framework for data augmentation F Isensee, P Jäger, J Wasserthal, D Zimmerer, J Petersen, S Kohl, ... Zenodo, 3632567, 2020 | 36* | 2020 |
The federated tumor segmentation (fets) challenge S Pati, U Baid, M Zenk, B Edwards, M Sheller, GA Reina, P Foley, ... arXiv preprint arXiv:2105.05874, 2021 | 28 | 2021 |
Medical out-of-distribution analysis challenge (Mar 2020) D Zimmerer, J Petersen, G Köhler, P Jäger, P Full, T Roß, T Adler, ... URL https://doi. org/10.5281/zenodo 3784230, 0 | 19* | |
A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients D Zimmerer, J Petersen, SAA Kohl, KH Maier-Hein Medical Imaging meets NeurIPS Workshop 2018, 2018 | 16 | 2018 |
Spiking convolutional deep belief networks J Kaiser, D Zimmerer, JCV Tieck, S Ulbrich, A Roennau, R Dillmann Artificial Neural Networks and Machine Learning–ICANN 2017: 26th …, 2017 | 12 | 2017 |
Robust environment perception for the audi autonomous driving cup F Kuhnt, M Pfeiffer, P Zimmer, D Zimmerer, JM Gomer, V Kaiser, ... 2016 IEEE 19th International Conference on Intelligent Transportation …, 2016 | 9 | 2016 |
MOOD 2020: A public Benchmark for Out-of-Distribution Detection and Localization on medical Images D Zimmerer, PM Full, F Isensee, P Jäger, T Adler, J Petersen, G Köhler, ... IEEE Transactions on Medical Imaging 41 (10), 2728-2738, 2022 | 7 | 2022 |
Continuous-time deep glioma growth models J Petersen, F Isensee, G Köhler, PF Jäger, D Zimmerer, U Neuberger, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 6 | 2021 |
High-and Low-level image component decomposition using VAEs for improved reconstruction and anomaly detection D Zimmerer, J Petersen, K Maier-Hein Medical Imaging meets NeurIPS Workshop 2019, 2019 | 5 | 2019 |
GP-ConvCNP: Better generalization for conditional convolutional Neural Processes on time series data J Petersen, G Köhler, D Zimmerer, F Isensee, PF Jäger, KH Maier-Hein Uncertainty in Artificial Intelligence, 939-949, 2021 | 2* | 2021 |
Realistic Evaluation of FixMatch on Imbalanced Medical Image Classification Tasks M Zenk, D Zimmerer, F Isensee, PF Jäger, J Wasserthal, K Maier-Hein Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on …, 2022 | 1 | 2022 |
Advanced deep learning methods P Jäger, F Isensee, J Petersen, D Zimmerer, J Wasserthal, KH Maier-Hein Bildverarbeitung für die Medizin 2018: Algorithmen-Systeme-Anwendungen …, 2018 | 1 | 2018 |
CRADL: Contrastive Representations for Unsupervised Anomaly Detection and Localization CT Lüth, D Zimmerer, G Koehler, PF Jaeger, F Isensee, J Petersen, ... arXiv preprint arXiv:2301.02126, 2023 | | 2023 |
Biomedical image analysis competitions: The state of current participation practice M Eisenmann, A Reinke, V Weru, MD Tizabi, F Isensee, TJ Adler, ... arXiv preprint arXiv:2212.08568, 2022 | | 2022 |
Unsupervised Anomaly Detection in the Wild D Zimmerer, D Paech, C Lüth, J Petersen, G Köhler, K Maier-Hein Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on …, 2022 | | 2022 |
Unsupervised Learning for Anomaly Detection in Medical Images D Zimmerer | | 2022 |