| Platelet aggregates detected using quantitative phase imaging associate with COVID-19 severity C Klenk, J Erber, D Fresacher, S Röhrl, M Lengl, D Heim, H Irl, M Schlegel, ... Communications Medicine 3 (1), 161, 2023 | 15 | 2023 |
| Towards Cross Domain Transfer Learning for Underwater Correspondence Search P Schöntag, D Nakath, S Röhrl, K Köser International Conference on Image Analysis and Processing, 461-472, 2022 | 9 | 2022 |
| A Comparison of Uncertainty Quantification Methods for Active Learning in Image Classification A Hein, S Röhrl, T Grobel, M Lengl, N Hafez, M Knopp, C Klenk, D Heim, ... 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 7 | 2022 |
| Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis S Röhrl, A Hein, L Huang, D Heim, C Klenk, M Lengl, M Knopp, N Hafez, ... arXiv preprint arXiv:2208.08834, 2022 | 4 | 2022 |
| Analysis of different methods to close the reality gap for instance segmentation in a flexible assembly cell N Hafez, V Dietrich, S Roehrl International Conference on Robotics in Alpe-Adria Danube Region, 505-515, 2020 | 4 | 2020 |
| Explainable Feature Learning with Variational Autoencoders for Holographic Image Analysis S Röhrl, L Bernhard, M Lengl, C Klenk, D Heim, M Knopp, S Schumann, ... Proceedings of the 16th International Joint Conference on Biomedical …, 2023 | 3 | 2023 |
| Measurement of Platelet Aggregation in Ageing Samples and After in-Vitro Activation C Klenk, D Fresacher, S Röhrl, D Heim, M Lengl, S Schumann, M Knopp, ... Proceedings of the 16th International Joint Conference on Biomedical …, 2023 | 3 | 2023 |
| Digital Holographic Microscopy in Veterinary Medicine—A Feasibility Study to Analyze Label-Free Leukocytes in Blood and Milk of Dairy Cows S Farschtschi, M Lengl, S Röhrl, C Klenk, O Hayden, K Diepold, MW Pfaffl Animals 14 (21), 3156, 2024 | 2 | 2024 |
| Composition Counts: A Machine Learning View on Immunothrombosis using Quantitative Phase Imaging D Fresacher, S Röhrl, C Klenk, J Erber, H Irl, D Heim, M Lengl, ... Machine Learning for Healthcare Conference, 208-229, 2023 | 2 | 2023 |
| Towards Interpretable Classification of Leukocytes based on Deep Learning S Röhrl, J Groll, M Lengl, S Schumann, C Klenk, D Heim, M Knopp, ... ICML 3rd Workshop on Interpretable Machine Learning in Healthcare (IMLH), 2023 | 2 | 2023 |
| Iterative Visual Interaction with Latent Diffusion Models L Sacchetto, S Röhrl, K Diepold International Conference on Human-Computer Interaction, 411-421, 2024 | 1 | 2024 |
| Explainable Artificial Intelligence for Cytological Image Analysis S Röhrl, H Maier, M Lengl, C Klenk, D Heim, M Knopp, S Schumann, ... International Conference on Artificial Intelligence in Medicine, 75-85, 2023 | 1 | 2023 |
| Autoencoder Features for Differentiation of Leukocytes Based on Digital Holographic Microscopy (DHM) S Röhrl, M Ugele, C Klenk, D Heim, O Hayden, K Diepold Computer Aided Systems Theory–EUROCAST 2019: 17th International Conference …, 2020 | 1 | 2020 |
| Homomorphic Encryption in Federated Medical Image Classification M Lengl, S Schumann, S Röhrl, O Hayden, K Diepold 2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC), 1-6, 2025 | | 2025 |
| Feature Extraction by Image Transformations for Cell Image Classification S Röhrl, F Steinmetz, P Paukner, M Lengl, S Schumann, D Fresacher, ... 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2024 | | 2024 |
| DETECTION OF MOLECULAR BIOLOGICAL OBJECTS, CELLULAR BIOLOGICAL OBJECTS AND CELL AGGREGATES USING QUANTITATIVE PHASE-CONTRAST MICROSCOPY O Hayden, C Klenk, S Röhrl US Patent App. 18/291,943, 2024 | | 2024 |
| Detection of cell aggregates using quantitative phase-contrast microscopy O Hayden, J Erber, S Rasch, T Lahmer, S Röhrl, C Klenk US Patent App. 18/291,952, 2024 | | 2024 |
| ‘Komputer & Creativität’Teaching at the Intersection of Engineering, AI, and Theater S Röhrl, S Schwarzmeier, D Fresacher, L Sacchetto, A Strasser, ... 2024 IEEE 3rd German Education Conference (GECon), 1-6, 2024 | | 2024 |
| Immunothrombosis of acute care patients quantified with phase imaging flow cytometry O Hayden, K Diepold, C Klenk, J Erber, D Fresacher, S Röhrl, M Lengl, ... Quantitative Phase Imaging X, PC128520I, 2024 | | 2024 |
| A Transparent View on Machine Learning for Holographic Cytology SJ Röhrl Technische Universität München, 2024 | | 2024 |
Klaus DiepoldTechnical University of Munich, Dept. Computer EngineeringBestätigte E-Mail-Adresse bei tum.de
