| Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels S Hansen, S Gautam, R Jenssen, M Kampffmeyer Medical Image Analysis 78, 102385, 2022 | 86 | 2022 |
| This looks more like that: Enhancing self-explaining models by prototypical relevance propagation S Gautam, MMC Höhne, S Hansen, R Jenssen, M Kampffmeyer Pattern Recognition 136, 109172, 2023 | 44 | 2023 |
| Protovae: A trustworthy self-explainable prototypical variational model S Gautam, A Boubekki, S Hansen, S Salahuddin, R Jenssen, M Höhne, ... Advances in Neural Information Processing Systems 35, 17940-17952, 2022 | 34 | 2022 |
| Unsupervised supervoxel-based lung tumor segmentation across patient scans in hybrid PET/MRI S Hansen, S Kuttner, M Kampffmeyer, TV Markussen, R Sundset, SK Øen, ... Expert Systems with Applications 167, 114244, 2021 | 16 | 2021 |
| ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement S Hansen, S Gautam, SA Salahuddin, M Kampffmeyer, R Jenssen Medical Image Analysis 89, 102870, 2023 | 10 | 2023 |
| Demonstrating the risk of imbalanced datasets in chest x-ray image-based diagnostics by prototypical relevance propagation S Gautam, MMC Höhne, S Hansen, R Jenssen, M Kampffmeyer 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 1-5, 2022 | 10 | 2022 |
| Data-centric machine learning for geospatial remote sensing data R Roscher, M Rußwurm, C Gevaert, M Kampffmeyer, JA Santos, ... arXiv preprint arXiv:2312.05327, 2023 | 6 | 2023 |
| A self-guided anomaly detection-inspired few-shot segmentation network SA Salahuddin, S Hansen, S Gautam, M Kampffmeyer, R Jenssen CEUR Workshop Proceedings, 2022 | 3* | 2022 |
| Self-supervised few-shot learning for ischemic stroke lesion segmentation L Tomasetti, S Hansen, M Khanmohammadi, K Engan, LJ Høllesli, ... 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 1-5, 2023 | 2 | 2023 |
| Leveraging supervoxels for medical image volume segmentation with limited supervision S Hansen UiT Norges arktiske universitet, 2022 | 1 | 2022 |
| Better, Not Just More: Data-Centric Machine Learning for Earth Observation R Roscher, M Rußwurm, C Gevaert, M Kampffmeyer, JA dos Santos, ... | | 2024 |
| This looks More Like that: Enhancing Self-Explaining Models by prototypical relevance propagation: This Looks More Like That S Gautam, MMC Höhne, S Hansen, R Jenssen, M Kampffmeyer Amsterdam: Elsevier, 2022 | | 2022 |
Michael Christian KampffmeyerProfessor in Machine Learning, UiT the Arctic University of Norway, Tromsø在 post.uit.no 的电子邮件经过验证
