| U-net: Convolutional networks for biomedical image segmentation O Ronneberger, P Fischer, T Brox Medical image computing and computer-assisted intervention–MICCAI 2015: 18th …, 2015 | 96889 | 2015 |
| Highly accurate protein structure prediction with AlphaFold J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... nature 596 (7873), 583-589, 2021 | 29079 | 2021 |
| 3D U-Net: learning dense volumetric segmentation from sparse annotation Ö Çiçek, A Abdulkadir, SS Lienkamp, T Brox, O Ronneberger Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th …, 2016 | 8127 | 2016 |
| Clinically applicable deep learning for diagnosis and referral in retinal disease J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ... Nature medicine 24 (9), 1342-1350, 2018 | 2481 | 2018 |
| Highly accurate protein structure prediction for the human proteome K Tunyasuvunakool, J Adler, Z Wu, T Green, M Zielinski, A Žídek, ... Nature 596 (7873), 590-596, 2021 | 2372 | 2021 |
| Protein complex prediction with AlphaFold-Multimer R Evans, M O’Neill, A Pritzel, N Antropova, A Senior, T Green, A Žídek, ... biorxiv, 2021.10. 04.463034, 2021 | 2307 | 2021 |
| Medical image computing and computer-assisted intervention–MICCAI 2015 O Ronneberger, P Fischer, T Brox, N Navab, J Hornegger, WM Wells, ... Lecture Notes in Computer Science 9351, 234-241, 2015 | 2155 | 2015 |
| U-Net: deep learning for cell counting, detection, and morphometry T Falk, D Mai, R Bensch, Ö Çiçek, A Abdulkadir, Y Marrakchi, A Böhm, ... Nature methods 16 (1), 67-70, 2019 | 1853 | 2019 |
| Accurate structure prediction of biomolecular interactions with AlphaFold 3 J Abramson, J Adler, J Dunger, R Evans, T Green, A Pritzel, ... Nature, 1-3, 2024 | 1381 | 2024 |
| A large annotated medical image dataset for the development and evaluation of segmentation algorithms AL Simpson, M Antonelli, S Bakas, M Bilello, K Farahani, B Van Ginneken, ... arXiv preprint arXiv:1902.09063, 2019 | 1064 | 2019 |
| The medical segmentation decathlon M Antonelli, A Reinke, S Bakas, K Farahani, A Kopp-Schneider, ... Nature communications 13 (1), 4128, 2022 | 980 | 2022 |
| Gland segmentation in colon histology images: The glas challenge contest K Sirinukunwattana, JPW Pluim, H Chen, X Qi, PA Heng, YB Guo, ... Medical image analysis 35, 489-502, 2017 | 862 | 2017 |
| U-Net: Convolutional networks for biomedical image segmentation. arXiv 2015 O Ronneberger, P Fischer, T Brox arXiv preprint arXiv:1505.04597, 2015 | 653 | 2015 |
| A probabilistic u-net for segmentation of ambiguous images S Kohl, B Romera-Paredes, C Meyer, J De Fauw, JR Ledsam, ... Advances in neural information processing systems 31, 2018 | 634 | 2018 |
| U-Net: Convolutional networks for biomedical image segmentation, arXiv: e-print service O Ronneberger, P Fischer, T Brox arXiv preprint arXiv:1505.04597, 2015 | 634 | 2015 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 614 | 2024 |
| An objective comparison of cell-tracking algorithms V Ulman, M Maška, KEG Magnusson, O Ronneberger, C Haubold, ... Nature methods 14 (12), 1141-1152, 2017 | 602 | 2017 |
| A new fate mapping system reveals context-dependent random or clonal expansion of microglia TL Tay, D Mai, J Dautzenberg, F Fernández-Klett, G Lin, null Sagar, ... Nature neuroscience 20 (6), 793-803, 2017 | 567 | 2017 |
| A benchmark for comparison of dental radiography analysis algorithms CW Wang, CT Huang, JH Lee, CH Li, SW Chang, MJ Siao, TM Lai, ... Medical image analysis 31, 63-76, 2016 | 381 | 2016 |
| Medical image computing and computer-assisted intervention–MICCAI 2016 Ö Çiçek, A Abdulkadir, SS Lienkamp, T Brox, O Ronneberger 19th International Conference, Athens, Greece, 424-432, 2016 | 373* | 2016 |
Thomas BroxUniversity of FreiburgEmail verificata su cs.uni-freiburg.de
