| Reproducible biofilm cultivation of chemostat-grown Escherichia coli and investigation of bacterial adhesion on biomaterials using a non-constant-depth film fermenter C Lüdecke, KD Jandt, D Siegismund, MJ Kujau, E Zang, M Rettenmayr, ... PloS one 9 (1), e84837, 2014 | 79 | 2014 |
| Quantification of the interaction between biomaterial surfaces and bacteria by 3-D modeling D Siegismund, A Undisz, S Germerodt, S Schuster, M Rettenmayr Acta biomaterialia 10 (1), 267-275, 2014 | 57 | 2014 |
| Evaluation of wettability and surface energy of native Nitinol surfaces in relation to hemocompatibility SA Shabalovskaya, D Siegismund, E Heurich, M Rettenmayr Materials Science and Engineering: C 33 (1), 127-132, 2013 | 30 | 2013 |
| Fibrinogen adsorption on biomaterials–a numerical study D Siegismund, TF Keller, KD Jandt, M Rettenmayr Macromolecular bioscience 10 (10), 1216-1223, 2010 | 28 | 2010 |
| Know when you don't know: a robust deep learning approach in the presence of unknown phenotypes O Dürr, E Murina, D Siegismund, V Tolkachev, S Steigele, B Sick Assay and drug development technologies 16 (6), 343-349, 2018 | 21 | 2018 |
| Developing deep learning applications for life science and pharma industry D Siegismund, V Tolkachev, S Heyse, B Sick, O Duerr, S Steigele Drug research 68 (06), 305-310, 2018 | 14 | 2018 |
| Deep learning-based HCS image analysis for the enterprise S Steigele, D Siegismund, M Fassler, M Kustec, B Kappler, T Hasaka, ... SLAS DISCOVERY: Advancing the Science of Drug Discovery 25 (7), 812-821, 2020 | 12 | 2020 |
| Benchmarking feature selection methods for compressing image information in high-content screening D Siegismund, M Fassler, S Heyse, S Steigele SLAS technology 27 (1), 85-93, 2022 | 10 | 2022 |
| Quantitative modeling of fibrinogen adsorption on different biomaterials D Siegismund, A Schroeter, S Schuster, M Rettenmayr Cellular and Molecular Bioengineering 6, 210-219, 2013 | 6 | 2013 |
| Self-supervised representation learning for high-content screening D Siegismund, M Wieser, S Heyse, S Steigele International Conference on Medical Imaging with Deep Learning, 1108-1124, 2022 | 5 | 2022 |
| Discrimination between random and non-random processes in early bacterial colonization on biomaterial surfaces: application of point pattern analysis D Siegismund, A Schroeter, C Lüdecke, A Undisz, KD Jandt, M Roth, ... Biofouling 30 (9), 1023-1033, 2014 | 5 | 2014 |
| Uncertainty with deep learning: a practical view on out of distribution detection D Siegismund, S Heyse, S Steigele 2020 7th Swiss Conference on Data Science (SDS), 65-66, 2020 | 3 | 2020 |
| Learning Channel Importance for High Content Imaging with Interpretable Deep Input Channel Mixing D Siegismund, M Wieser, S Heyse, S Steigele DAGM German Conference on Pattern Recognition, 335-347, 2023 | 2 | 2023 |
| Vision transformers show improved robustness in high-content image analysis M Wieser, D Siegismund, S Heyse, S Steigele 2022 9th Swiss Conference on Data Science (SDS), 71-72, 2022 | 2 | 2022 |
| PCIM: Learning Pixel Attributions via Pixel-wise Channel Isolation Mixing in High Content Imaging D Siegismund, M Wieser, S Heyse, S Steigele arXiv preprint arXiv:2412.02275, 2024 | | 2024 |
| Author's personal copy D SIEGISMUND, A SCHROETER, S SCHUSTER, M RETTENMAYR Adsorption Processes of Proteins and Bacteria on Biomaterial Surfaces …, 2015 | | 2015 |
| Adsorption processes of proteins and bacteria on biomaterial surfaces: insight from modeling D Siegismund | | 2015 |
| Roost, Dano 63 Rosso, Paolo 29 Sassik, Bernhard 1 Schenck, Wolfram 61 L Barth, A Bhardwaj, SA Bigdeli, M Bravin, M Christen, P Cudré-Mauroux, ... | | |
Andreas UndiszTU Chemnitz在 mb.tu-chemnitz.de 的电子邮件经过验证
