| Reconstruction of initial pressure from limited view photoacoustic images using deep learning D Waibel, J Gröhl, F Isensee, T Kirchner, K Maier-Hein, L Maier-Hein Photons Plus Ultrasound: Imaging and Sensing 2018 10494, 196-203, 2018 | 113 | 2018 |
| A diffusion model predicts 3d shapes from 2d microscopy images DJE Waibel, E Röell, B Rieck, R Giryes, C Marr 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 1-5, 2023 | 39 | 2023 |
| Make deep learning algorithms in computational pathology more reproducible and reusable SJ Wagner, C Matek, S Shetab Boushehri, M Boxberg, L Lamm, A Sadafi, ... Nature Medicine 28 (9), 1744-1746, 2022 | 19 | 2022 |
| Capturing Shape Information with Multi-scale Topological Loss Terms for 3D Reconstruction DJE Waibel, S Atwell, M Meier, C Marr, B Rieck Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 …, 2022 | 18 | 2022 |
| InstantDL-An easy-to-use deep learning pipeline for image segmentation and classification DJE Waibel, SS Boushehri, C Marr BMC Bioinformatics 22 (1), 1-15, 2021 | 18 | 2021 |
| Systematic Comparison of Incomplete-Supervision Approaches for Biomedical Image Classification S Shetab Boushehri, AB Qasim, D Waibel, F Schmich, C Marr International Conference on Artificial Neural Networks, 355-365, 2022 | 14* | 2022 |
| Built to last? Reproducibility and reusability of deep learning algorithms in computational pathology SJ Wagner, C Matek, SS Boushehri, M Boxberg, L Lamm, A Sadafi, ... Modern Pathology 37 (1), 100350, 2024 | 13 | 2024 |
| Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip S Atwell, DJE Waibel, SS Boushehri, S Wiedenmann, C Marr, M Meier Cell Reports Methods, 2023 | 9 | 2023 |
| SHAPR predicts 3D cell shapes from 2D microscopic images DJE Waibel, N Kiermeyer, S Atwell, A Sadafi, M Meier, C Marr Iscience 25 (11), 2022 | 7 | 2022 |
| Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease B Mairhörmann, A Castelblanco, F Häfner, V Koliogiannis, L Haist, ... Radiology: Artificial Intelligence, e220239, 2023 | 6* | 2023 |
| Photoacoustic image reconstruction to solve the acoustic inverse problem with deep learning DJE Waibel Master thesis, 2018 | 6 | 2018 |
| In-silico staining from bright-field and fluorescent images using deep learning DJE Waibel, U Tiemann, V Lupperger, H Semb, C Marr Artificial Neural Networks and Machine Learning–ICANN 2019: Image Processing …, 2019 | 3 | 2019 |
| Utilizing mask-guided cross-image attention for zero-shot in-silico histopathologic image generation with a diffusion model D Winter, N Triltsch, M Rosati, A Shumilov, Z Kokaragac, Y Popov, ... arXiv preprint arXiv:2407.11664, 2024 | 2 | 2024 |
| Auxiliary CycleGAN-guidance for Task-Aware Domain Translation from Duplex to Monoplex IHC Images N Brieu, N Triltsch, P Wortmann, D Winter, S Saran, M Rebelatto, ... arXiv preprint arXiv:2403.07389, 2024 | 2 | 2024 |
| ReStainGAN: Leveraging IHC to IF Stain Domain Translation for in-silico Data Generation D Winter, N Triltsch, P Plewa, M Rosati, T Padel, R Hill, M Schick, N Brieu arXiv preprint arXiv:2403.06545, 2024 | 1 | 2024 |
| PXPermute reveals staining importance in multichannel imaging flow cytometry SS Boushehri, A Kornivetc, DJE Winter, S Kazeminia, K Essig, F Schmich, ... Cell Reports Methods 4 (2), 2024 | | 2024 |
| Deep learning methods for single cell morphometry in high-throughput microscopy D Waibel Dissertation, 2023 | | 2023 |
| Reproducibility in the context of AI in health care S Wagner, C Matek, SS Boushehri, M Boxberg, L Lamm, A Sadafi, ... Helmholtz Open Science Office, 2023 | | 2023 |
| Datasets for 3D shape reconstruction from 2D microscopy images D Waibel, N Kiermeyer, S Atwell, B Rieck, M Meier, C Marr https://doi.org/10.5281/zenodo.7031924, 2022 | | 2022 |
| Datasets for 'Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip' MM Scott Atwell, Dominik Waibel, Sayedali Shetab Boushehri, Carsten Marr https://doi.org/10.5281/zenodo.7014598, 2022 | | 2022 |
Carsten MarrInstitute of AI for Health, Helmholtz Zentrum München在 helmholtz-muenchen.de 的电子邮件经过验证
