| A comparison of methods for fully automatic segmentation of tumors and involved nodes in PET/CT of head and neck cancers AR Groendahl, IS Knudtsen, BN Huynh, M Mulstad, YM Moe, F Knuth, ... Physics in Medicine & Biology 66 (6), 065012, 2021 | 41 | 2021 |
| Link-INVENT: generative linker design with reinforcement learning J Guo, F Knuth, C Margreitter, JP Janet, K Papadopoulos, O Engkvist, ... Digital Discovery 2 (2), 392-408, 2023 | 22 | 2023 |
| MRI-based automatic segmentation of rectal cancer using 2D U-Net on two independent cohorts F Knuth, IA Adde, BN Huynh, AR Groendahl, RM Winter, A Negård, ... Acta Oncologica 61 (2), 255-263, 2022 | 20 | 2022 |
| Semi-automatic tumor segmentation of rectal cancer based on functional magnetic resonance imaging F Knuth, AR Groendahl, RM Winter, T Torheim, A Negård, SH Holmedal, ... Physics and imaging in radiation oncology 22, 77-84, 2022 | 3 | 2022 |
| Quantitative MRI-based radiomics analysis identifies blood flow feature associated to overall survival for rectal cancer patients F Knuth, F Tohidinezhad, RM Winter, KM Bakke, A Negård, SH Holmedal, ... Scientific Reports 14 (1), 258, 2024 | | 2024 |
| OC-0517 Automatic tumor delineation in rectal cancer using functional MRI and machine learning F Knuth, AR Grøndahl, T Torheim, A Negård, SH Holmedal, KM Bakke, ... Radiotherapy and Oncology 133, S269-S270, 2019 | | 2019 |
| The data acquisition system of the fluorescence telescope FAMOUS T Niggemann, J Auffenberg, T Bretz, T Hebbeker, F Knuth, M Lauscher, ... Verhandlungen der Deutschen Physikalischen Gesellschaft, 2015 | | 2015 |
| Deep Learning Segmentation of Rectal Cancer on MRI E Grøvik, D Yi, F Knuth, S Meltzer, A Negård, KR Redalen | | |
