| Computed tomography reconstruction using deep image prior and learned reconstruction methods DO Baguer, J Leuschner, M Schmidt Inverse Problems 36 (9), 094004, 2020 | 161 | 2020 |
| Regularization by architecture: A deep prior approach for inverse problems S Dittmer, T Kluth, P Maass, D Otero Baguer Journal of Mathematical Imaging and Vision 62, 456-470, 2020 | 101 | 2020 |
| LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction J Leuschner, M Schmidt, DO Baguer, P Maass Scientific Data 8 (1), 109, 2021 | 70 | 2021 |
| The lodopab-ct dataset: A benchmark dataset for low-dose ct reconstruction methods J Leuschner, M Schmidt, DO Baguer, P Maaß arXiv preprint arXiv:1910.01113, 2019 | 47 | 2019 |
| Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of basal cell carcinoma J Le’Clerc Arrastia, N Heilenkötter, D Otero Baguer, L Hauberg-Lotte, ... Journal of imaging 7 (4), 71, 2021 | 42 | 2021 |
| Evaluation of a deep learning approach to differentiate Bowen’s disease and seborrheic keratosis P Jansen, DO Baguer, N Duschner, J Le’Clerc Arrastia, M Schmidt, ... Cancers 14 (14), 3518, 2022 | 13 | 2022 |
| A deep prior approach to magnetic particle imaging S Dittmer, T Kluth, DO Baguer, P Maass Machine Learning for Medical Image Reconstruction: Third International …, 2020 | 12 | 2020 |
| Connection between shock wave induced indentations and hardness by means of neural networks T Czotscher, DO Baguer, F Vollertsen, I Piotrowska-Kurczewski, P Maaß AIP Conference Proceedings 2113 (1), 2019 | 9 | 2019 |
| Deep learning detection of melanoma metastases in lymph nodes P Jansen, DO Baguer, N Duschner, JLC Arrastia, M Schmidt, J Landsberg, ... European Journal of Cancer 188, 161-170, 2023 | 6 | 2023 |
| Staincut: stain normalization with contrastive learning JCG Pérez, DO Baguer, P Maass Journal of Imaging 8 (7), 2022 | 6 | 2022 |
| Inverse problems in designing new structural materials DO Baguer, I Piotrowska-Kurczewski, P Maass Modeling, Simulation and Optimization of Complex Processes HPSC 2018 …, 2021 | 5 | 2021 |
| Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma N Duschner, DO Baguer, M Schmidt, KG Griewank, E Hadaschik, ... JDDG: Journal der Deutschen Dermatologischen Gesellschaft 21 (11), 1329-1337, 2023 | 3 | 2023 |
| Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI C Janßen, T Boskamp, J Le’Clerc Arrastia, D Otero Baguer, ... Cancers 14 (24), 6181, 2022 | 3 | 2022 |
| Deep learning based histological classification of adnex tumors P Jansen, JLC Arrastia, DO Baguer, M Schmidt, J Landsberg, J Wenzel, ... European Journal of Cancer 196, 113431, 2024 | 1 | 2024 |
| Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time R Herdt, M Schmidt, DO Baguer, JLC Arrastia, P Maass arXiv preprint arXiv:2302.02181, 2023 | 1 | 2023 |
| Neural Networks for solving Inverse Problems: Applications in Materials Science and Medical Imaging DO Baguer Universität Bremen, 2020 | 1 | 2020 |
| The LoDoPaB-CT Dataset J Leuschner, M Schmidt, DO Baguer, P Maaß arXiv preprint arXiv:1910.01113, 2019 | 1 | 2019 |
| Smooth Deep Saliency R Herdt, M Schmidt, DO Baguer, P Maaß arXiv preprint arXiv:2404.02282, 2024 | | 2024 |
| How GAN Generators can Invert Networks in Real-Time R Herdt, M Schmidt, DO Baguer, JLC Arrastia, P Maaß Asian Conference on Machine Learning, 422-437, 2024 | | 2024 |
| Einsatz künstlicher Intelligenz mittels Deep Learning in der dermatopathologischen Routinediagnostik des Basalzellkarzinoms: Applying an artificial intelligence … N Duschner, DO Baguer, M Schmidt, KG Griewank, E Hadaschik, ... JDDG: Journal der Deutschen Dermatologischen Gesellschaft 21 (11), 1329-1338, 2023 | | 2023 |
