| 3D Self-Supervised Methods for Medical Imaging A Taleb, W Loetzsch, N Danz, J Severin, T Gaertner, B Bergner, C Lippert Proceedings of NeurIPS 2020, 2020 | 212 | 2020 |
| HPI-DHC at TREC 2018 Precision Medicine Track. M Oleynik, E Faessler, AM Sasso, A Kappattanavar, B Bergner, ... TREC, 2018 | 19 | 2018 |
| Face mask detector C Lippert, B Bergner, A Ahmed, R Ali, S Adeel, MH Shahriar, ... University of Potsdam, 2020 | 18 | 2020 |
| Self-supervised learning methods for label-efficient dental caries classification A Taleb, C Rohrer, B Bergner, G De Leon, JA Rodrigues, F Schwendicke, ... Diagnostics 12 (5), 1237, 2022 | 16 | 2022 |
| Iterative Patch Selection for High-Resolution Image Recognition B Bergner, C Lippert, A Mahendran The Eleventh International Conference on Learning Representations (ICLR 2023), 2022 | 9 | 2022 |
| Less Is More: A Comparison of Active Learning Strategies for 3D Medical Image Segmentation JM Burmeister, MF Rosas, J Hagemann, J Kordt, J Blum, S Shabo, ... ICML 2022 Workshop on Adaptive Experimental Design and Active Learning in …, 2022 | 6 | 2022 |
| MORPHER-A Platform to Support Modeling of Outcome and Risk Prediction in Health Research HF Da Cruz, B Bergner, O Konak, F Schneider, P Bode, C Lempert, ... 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering …, 2019 | 3 | 2019 |
| Interpretable and interactive deep multiple instance learning for dental caries classification in bitewing X-rays B Bergner, C Rohrer, A Taleb, M Duchrau, G De Leon, JA Rodrigues, ... Medical Imaging with Deep Learning (MIDL 2022), 2021 | 2 | 2021 |
| An engineering approach towards multi-site virtual molecular tumor board software R Henkenjohann, B Bergner, F Borchert, N Bougatf, H Hund, R Eils, ... International Conference on ICT for Health, Accessibility and Wellbeing, 156-170, 2021 | 2 | 2021 |
| Active Subtopic Detection in Multitopic Data. B Bergner, G Krempl AL@ iKNOW, 35-44, 2016 | 2 | 2016 |
| Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding B Bergner, A Skliar, A Royer, T Blankevoort, Y Asano, BE Bejnordi arXiv preprint arXiv:2402.16844, 2024 | | 2024 |
| MACHINE LEARNING DETECTS ASSOCIATIONS BETWEEN RETINA FEATURES AND BLOOD PRESSURE STATUS IN RICE DIET PROGRAM PATIENTS R Sommerfeld, C Lorenz, D Lopez, A Kuo, B Bergner, FCC Luft, C Lippert, ... HYPERTENSION 79, 2022 | | 2022 |
| Abstract P305: MACHINE LEARNING DETECTS ASSOCIATIONS BETWEEN RETINA FEATURES AND BLOOD PRESSURE STATUS IN RICE DIET PROGRAM PATIENTS R Sommerfeld, C Lorenz, D Lopez, A Kuo, B Bergner, FCC Luft, C Lippert, ... Hypertension 79 (Suppl_1), AP305-AP305, 2022 | | 2022 |
| Deep Learning Models for 3D MRI Brain Classification: A Multi-sequence Comparison M Pullig, B Bergner, A Doshi, A Hennemuth, ZA Fayad, C Lippert Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on …, 2022 | | 2022 |
| Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification. Diagnostics 2022, 12, 1237 A Taleb, C Rohrer, B Bergner, G De Leon, JA Rodrigues, F Schwendicke, ... s Note: MDPI stays neutral with regard to jurisdictional claims in published …, 2022 | | 2022 |
| HistoFlow: Label-Efficient and Interactive Deep Learning Cell Analysis T Henning, B Bergner, C Lippert bioRxiv, 2020.07. 15.204891, 2020 | | 2020 |
| Appendix for: 3D Self-Supervised Methods for Medical Imaging A Taleb, W Loetzsch, N Danz, J Severin, T Gaertner, B Bergner, C Lippert | | |
| The Regularizing Effect of Different Output Layer Designs in Deep Neural Networks B Bergner, C Lippert | | |
Christoph LippertProfessor, Hasso Plattner Institute, Universität Potsdam在 hpi.de 的电子邮件经过验证
