| Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge S Bakas, M Reyes, A Jakab, S Bauer, M Rempfler, A Crimi, RT Shinohara, ... arXiv preprint arXiv:1811.02629, 2018 | 1766 | 2018 |
| The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680, 2023 | 932 | 2023 |
| The medical segmentation decathlon M Antonelli, A Reinke, S Bakas, K Farahani, A Kopp-Schneider, ... Nature communications 13 (1), 4128, 2022 | 643 | 2022 |
| Modeling the distribution of normal data in pre-trained deep features for anomaly detection O Rippel, P Mertens, D Merhof 2020 25th International Conference on Pattern Recognition (ICPR), 6726-6733, 2021 | 218 | 2021 |
| Segmentation of brain tumors and patient survival prediction: Methods for the brats 2018 challenge L Weninger, O Rippel, S Koppers, D Merhof Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries …, 2019 | 89 | 2019 |
| Image-based survival prediction for lung cancer patients using CNNS C Haarburger, P Weitz, O Rippel, D Merhof 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019 …, 2019 | 56 | 2019 |
| Gaussian anomaly detection by modeling the distribution of normal data in pretrained deep features O Rippel, P Mertens, E König, D Merhof IEEE Transactions on Instrumentation and Measurement 70, 1-13, 2021 | 44 | 2021 |
| Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status T Piotrowski, O Rippel, A Elanzew, B Nießing, S Stucken, S Jung, N König, ... Computers in biology and medicine 129, 104172, 2021 | 34 | 2021 |
| The StemCellFactory: a modular system integration for automated generation and expansion of human induced pluripotent stem cells A Elanzew, B Nießing, D Langendoerfer, O Rippel, T Piotrowski, F Schenk, ... Frontiers in bioengineering and biotechnology 8, 580352, 2020 | 32 | 2020 |
| Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach J Thüring, O Rippel, C Haarburger, D Merhof, P Schad, P Bruners, ... European Radiology Experimental 4, 1-9, 2020 | 15 | 2020 |
| Transfer learning gaussian anomaly detection by fine-tuning representations O Rippel, A Chavan, C Lei, D Merhof arXiv preprint arXiv:2108.04116, 2021 | 13 | 2021 |
| GAN-based defect synthesis for anomaly detection in fabrics O Rippel, M Müller, D Merhof 2020 25th IEEE International Conference on Emerging Technologies and Factory …, 2020 | 13 | 2020 |
| Optimizing patchcore for few/many-shot anomaly detection J Santos, T Tran, O Rippel arXiv preprint arXiv:2307.10792, 2023 | 4 | 2023 |
| Anomaly Detection for Automated Visual Inspection: A Review O Rippel, D Merhof Bildverarbeitung in der Automation: Ausgewählte Beiträge des …, 2023 | 4 | 2023 |
| Anomaly detection for the automated visual inspection of pet preform closures O Rippel, P Haumering, J Brauers, D Merhof 2021 26th IEEE international conference on emerging technologies and factory …, 2021 | 4 | 2021 |
| Estimating the Probability Density Function of New Fabrics for Fabric Anomaly Detection. O Rippel, M Müller, A Münkel, T Gries, D Merhof ICPRAM, 463-470, 2021 | 4 | 2021 |
| Increasing the generalization of supervised fabric anomaly detection methods to unseen fabrics O Rippel, C Zwinge, D Merhof Sensors 22 (13), 4750, 2022 | 3 | 2022 |
| Leveraging pre-trained segmentation networks for anomaly segmentation O Rippel, D Merhof 2021 26th IEEE International Conference on Emerging Technologies and Factory …, 2021 | 3 | 2021 |
| AutoML Segmentation for 3D Medical Image Data: Contribution to the MSD Challenge 2018 O Rippel, L Weninger, D Merhof arXiv preprint arXiv:2005.09978, 2020 | 3 | 2020 |
| Panoptic segmentation of animal fibers O Rippel, N Schönfelder, K Rahimi, J Kurniadi, A Herrmann, D Merhof 2022 IEEE International Instrumentation and Measurement Technology …, 2022 | 2 | 2022 |
