| Deep representation learning for domain adaptable classification of infrared spectral imaging data AP Raulf, J Butke, C Küpper, F Großerueschkamp, K Gerwert, A Mosig Bioinformatics 36 (1), 287-294, 2020 | 24 | 2020 |
| Microsatellite instability (MSI-H) is associated with a high immunoscore but not with PD-L1 expression or increased survival in patients (pts.) with metastatic colorectal … S Noepel-Duennebacke, H Juette, K Schulmann, U Graeven, R Porschen, ... Journal of Cancer Research and Clinical Oncology 147, 3063-3072, 2021 | 11 | 2021 |
| Signaling pathways of heat- and hypersalinity-induced polyp bailout in Pocillopora acuta F Gösser, A Raulf, A Mosig, R Tollrian, M Schweinsberg Coral Reefs 40, 1713-1728, 2021 | 8 | 2021 |
| Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work S Theis, S Jentzsch, F Deligiannaki, C Berro, AP Raulf, C Bruder International Conference on Human-Computer Interaction, 355-380, 2023 | 5 | 2023 |
| A representation learning approach for recovering scatter‐corrected spectra from Fourier‐transform infrared spectra of tissue samples AP Raulf, J Butke, L Menzen, C Küpper, F Großerueschkamp, K Gerwert, ... Journal of Biophotonics 14 (3), e202000385, 2021 | 5 | 2021 |
| Tensor networks for quantum machine learning HM Rieser, F Koester, AP Raulf Proceedings Of The Royal Society A 479 (2275), 2023 | 4 | 2023 |
| Deep Neural Networks for the Correction of Mie Scattering in Fourier-Transformed Infrared Spectra of Biological Samples AP Raulf, J Butke, L Menzen, C Küpper, F Großerueschkamp, K Gerwert, ... arXiv preprint arXiv:2002.07681, 2020 | 4 | 2020 |
| Requirements for Explainability, Traceability, and Acceptance of Artificial Intelligence in Collaboration S Theis, S Jentzsch, F Deligiannaki, C Berro, AP Raulf, C Bruder 15th International Conference, VAMR 2023, Held as Part of the 25th HCI …, 2023 | 1 | 2023 |
| Potential analysis of a Quantum RL controller in the context of autonomous driving ML Hickmann, AP Raulf, F Köster, F Schwenker, HM Rieser 31st European Symposium on Artificial Neural Networks, Computational …, 2023 | 1 | 2023 |
| SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations. AP Raulf, S Däubener, B Hack, A Mosig, A Fischer ESANN, 2021 | 1 | 2021 |
| Revisiting the Evaluation of Deep Neural Networks for Pedestrian Detection. P Feifel, B Franke, AP Raulf, F Schwenker, F Bonarens, F Köster AISafety@ IJCAI, 2022 | | 2022 |
| A formative usability study of workflow management systems in label-free digital pathology M Jelonek, AP Raulf, E Fiala, J Butke, T Herrmann, A Mosig F1000Research 11, 192, 2022 | | 2022 |
| Signaling pathways of heat-and hypersalinity-induced polyp bailout in\(\textit {Pocillopora acuta}\) F Gösser, AP Raulf, A Mosig, R Tollrian, M Schweinsberg | | 2021 |
| Microsatellite instability (MSI-H) is associated with a high immunoscore but not with PD-L1 expression or increased survival in patients (pts.) with metastatic colorectal … S Nöpel-Dünnebacke, HJ Jütte, K Schulmann, U Graeven, R Porschen, ... | | 2021 |
| SmoothLRP: Smoothing Explanations of Neural Network Decisions by Averaging over Stochastic Input Variations AP Raulf, BL Hack, S Däubener, A Mosig, A Fischer | | 2020 |
| Studien zur Generalisierbarkeit künstlicher neuronaler Netze in der diagnostischen Infrarot-Spektroskopie AP Raulf Ruhr-Universität Bochum, 2020 | | 2020 |
| A prognostic gene signature for UICC II colorectal cancer patients with a high risk of recurrence by next generation sequencing S Kasimir, ST Liffers, AP Raulf, J Munding, M Karp, W Uhl, A Mosig, ... ONCOLOGY RESEARCH AND TREATMENT 39, 13-13, 2016 | | 2016 |
| Investigating the vulnerabilities and effects of prompt-tuning on pre-trained language models F Deligiannaki, AP Raulf | | |
