Publikationen

Self-Supervised Learning for Annotation Efficient Biomedical Image Segmentation
Rettenberger, L.; Schilling, M.; Elser, S.; Boehland, M.; Reischl, M.
2023. IEEE Transactions on Biomedical Engineering, 1–11. doi:10.1109/TBME.2023.3252889

EasyMLServe: Easy Development of REST Machine Learning Services
Neumann, O.; Schilling, M.; Reischl, M.; Mikut, R.
2022, Dezember. 32. Workshop Computational Intelligence (2022), Berlin, Deutschland, 1.–2. Dezember 2022

A Computational Workflow for Interdisciplinary Deep Learning Projects utilizing bwHPC Infrastructure
Schilling, M.; Neumann, O.; Scherr, T.; Cui, H.; Popova, A. A.; Levkin, P. A.; Götz, M.; Reischl, M.
2022. Proceedings of the 7th bwHPC Symposium, 69–74, Kommunikations- und Informationszentrum (kiz). doi:10.18725/OPARU-46069

Combining Miniaturized Chemical Synthesis with Biochemical Screeing for High-Throughput Discovery
Wiedmann, J. J.; Maier, T. C.; Schmidt, S.; Benz, M.; Schilling, M.; Czech, J.; Bärenz, F.; Mors, H.; Reischl, M.; Hopf, C.; Levkin, P. A.
2022, Oktober. Drug Discovery (ELRIG 2022), London, Vereinigtes Königreich, 4.–5. Oktober 2022

Annotation Efforts in Image Segmentation can be Reduced by Neural Network Bootstrapping
Rettenberger, L.; Schilling, M.; Reischl, M.
2022. Current Directions in Biomedical Engineering, 8 (2), 329–332. doi:10.1515/cdbme-2022-1084

Impact of Annotation Noise on Histopathology Nucleus Segmentation
Schilling, M. P.; Ahuja, N.; Rettenberger, L.; Scherr, T.; Reischl, M.
2022. Current Directions in Biomedical Engineering, 8 (2), 197–200. doi:10.1515/cdbme-2022-1051

microbeSEG dataset (1.0) [Data set]
Scherr, T.; Seiffarth, J.; Wollenhaupt, B.; Neumann, O.; Schilling, M.; Kohlheyer, D.; Scharr, H.; Mikut, R.; Nöh, K.
2022, April 28. doi:10.5281/zenodo.6497715

Tuning a Distance-Prediction-Based Cell Segmentation
Scherr, T.; Löffler, K.; Schilling, M.; Neumann, O.; Mikut, R.
2022, März 28. 20th IEEE International Symposium on Biomedical Imaging (ISBI 2022), Kalkutta, Indien, 28.–31. März 2022

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation
Scherr, T.; Seiffarth, J.; Wollenhaupt, B.; Neumann, O.; Schilling, M. P.; Kohlheyer, D.; Scharr, H.; Nöh, K.; Mikut, R.
2022. (A. Imran, Hrsg.) PLOS ONE, 17 (11), Art.-Nr.: e0277601. doi:10.1371/journal.pone.0277601

EasyMLServe: Easy Deployment of REST Machine Learning Services
Neumann, O.; Schilling, M.; Reischl, M.; Mikut, R.
2022. Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022. Hrsg.: H. Schulte; F. Hoffmann; R. Mikut, 11–30, KIT Scientific Publishing

KaIDA: a modular tool for assisting image annotation in deep learning
Schilling, M. P.; Schmelzer, S.; Klinger, L.; Reischl, M.
2022. Journal of Integrative Bioinformatics, 19 (4), 20220018. doi:10.1515/jib-2022-0018

Ciscnet - a Single-Branch Cell Nucleus Instance Segmentation and Classification Network
Böhland, M.; Neumann, O.; Schilling, M. P.; Reischl, M.; Mikut, R.; Loffler, K.; Scherr, T.
2022. 2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC), 1–5, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISBIC56247.2022.9854734

“Cells‐to‐cDNA on Chip”: Phenotypic assessment and gene expression analysis from live cells in nanoliter volumes using droplet microarrays
Chakraborty, S.; Luchena, C.; Elton, J. J.; Schilling, M. P.; Reischl, M.; Roux, M.; Levkin, P. A.; Popova, A. A.
2022. Advanced Healthcare Materials, 11 (12), Art.-Nr.: 2102493. doi:10.1002/adhm.202102493

Automated Annotator Variability Inspection for Biomedical Image Segmentation
Schilling, M. P.; Scherr, T.; Munke, F. R.; Neumann, O.; Schutera, M.; Mikut, R.; Reischl, M.
2022. IEEE access, 10, 2753–2765. doi:10.1109/ACCESS.2022.3140378

Grid Screener: A Tool for Automated High-throughput Screening on Biochemical and Biological Analysis Platforms
Schilling, M. P.; Schmelzer, S.; Gómez, J. E. U.; Popova, A. A.; Levkin, P. A.; Reischl, M.
2021. IEEE access, 9, 166027–166038. doi:10.1109/ACCESS.2021.3135709

Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks
Schilling, M. P.; Rettenberger, L.; Münke, F.; Cui, H.; Popova, A. A.; Levkin, P. A.; Mikut, R.; Reischl, M.
2021, November 26. 31. Workshop Computational Intelligence (2021), Berlin, Deutschland, 25.–26. November 2021

A Computational Workflow for Interdisciplinary Deep Learning Projects utilizing bwHPC Infrastructure
Schilling, M. P.; Neumann, O.; Scherr, T.; Cui, H.; Popova, A. A.; Levkin, P. A.; Götz, M.; Reischl, M.
2021, November 8. 7th bwHPC Symposium (2021), Online, 8. November 2021

Evaluierung von Merkmalen zur Abbildung von Veränderungen in ungeordneten Bilddaten = Evaluating features to represent changes in unstructured image data
Münke, F. R.; Schilling, M. P.; Mikut, R.; Reischl, M.
2021. Automatisierungstechnik, 69 (10), 892–902. doi:10.1515/auto-2021-0038

Label Assistant: A Workflow for Assisted Data Annotation in Image Segmentation Tasks
Schilling, M. P.; Rettenberger, L.; Münke, F.; Cui, H.; Popova, A. A.; Levkin, P. A.; Mikut, R.; Reischl, M.
2021. Proceedings - 31. Workshop Computational Intelligence : Berlin, 25. - 26. November 2021. Hrsg.: H. Schulte; F. Hoffmann; R. Mikut, 211–234, KIT Scientific Publishing

Radar Artifact Labeling Framework (RALF): Method for Plausible Radar Detections in Datasets
Isele, S.; Schilling, M.; Klein, F.; Saralajew, S.; Zoellner, J.
2021. Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS), April 28-30, 2021, 22–33, SciTePress. doi:10.5220/0010395100220033

Annotating Automotive Radar efficiently: Semantic Radar Labeling Framework (SeRaLF)
Isele, S. T.; Schilling, M. P.; Klein, F. E.; Zoellner, M. J.
2020. Conference on Neural Information Processing Systems, Sun Dec 6th through Sat the 12th