| Surgical data science for next-generation interventions L Maier-Hein, SS Vedula, S Speidel, N Navab, R Kikinis, A Park, ... Nature Biomedical Engineering 1 (9), 691-696, 2017 | 379 | 2017 |
| Why rankings of biomedical image analysis competitions should be interpreted with care L Maier-Hein, M Eisenmann, A Reinke, S Onogur, M Stankovic, P Scholz, ... Nature communications 9 (1), 5217, 2018 | 291 | 2018 |
| Surgical data science–from concepts toward clinical translation L Maier-Hein, M Eisenmann, D Sarikaya, K März, T Collins, A Malpani, ... Medical image analysis 76, 102306, 2022 | 181 | 2022 |
| Common limitations of image processing metrics: A picture story A Reinke, MD Tizabi, CH Sudre, M Eisenmann, T Rädsch, M Baumgartner, ... arXiv preprint arXiv:2104.05642, 2021 | 147 | 2021 |
| Can masses of non-experts train highly accurate image classifiers? A crowdsourcing approach to instrument segmentation in laparoscopic images L Maier-Hein, S Mersmann, D Kondermann, S Bodenstedt, A Sanchez, ... Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th …, 2014 | 101 | 2014 |
| Metrics reloaded: Pitfalls and recommendations for image analysis validation L Maier-Hein, B Menze arXiv. org, 2022 | 99 | 2022 |
| BIAS: Transparent reporting of biomedical image analysis challenges L Maier-Hein, A Reinke, M Kozubek, AL Martel, T Arbel, M Eisenmann, ... Medical image analysis 66, 101796, 2020 | 94 | 2020 |
| Methods and open-source toolkit for analyzing and visualizing challenge results M Wiesenfarth, A Reinke, BA Landman, M Eisenmann, LA Saiz, ... Scientific reports 11 (1), 2369, 2021 | 72 | 2021 |
| Heidelberg colorectal data set for surgical data science in the sensor operating room L Maier-Hein, M Wagner, T Ross, A Reinke, S Bodenstedt, PM Full, ... Scientific data 8 (1), 101, 2021 | 70 | 2021 |
| Surgical data science: enabling next-generation surgery L Maier-Hein, S Vedula, S Speidel, N Navab, R Kikinis, A Park, ... arXiv preprint arXiv:1701.06482, 2017 | 52 | 2017 |
| Large-scale medical image annotation with crowd-powered algorithms E Heim, T Roß, A Seitel, K März, B Stieltjes, M Eisenmann, J Lebert, ... Journal of Medical Imaging 5 (3), 034002-034002, 2018 | 48 | 2018 |
| How to exploit weaknesses in biomedical challenge design and organization A Reinke, M Eisenmann, S Onogur, M Stankovic, P Scholz, PM Full, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 43 | 2018 |
| A Delphi consensus statement for digital surgery K Lam, MD Abràmoff, JM Balibrea, SM Bishop, RR Brady, RA Callcut, ... NPJ digital medicine 5 (1), 100, 2022 | 36 | 2022 |
| Surgical data science–from concepts to clinical translation L Maier-Hein, M Eisenmann, D Sarikaya, K März, T Collins, A Malpani, ... arXiv preprint arXiv:2011.02284 2, 2020 | 29 | 2020 |
| Biomedical image analysis competitions: The state of current participation practice M Eisenmann, A Reinke, V Weru, MD Tizabi, F Isensee, TJ Adler, ... arXiv preprint arXiv:2212.08568, 2022 | 23 | 2022 |
| Understanding metric-related pitfalls in image analysis validation A Reinke, MD Tizabi, M Baumgartner, M Eisenmann, D Heckmann-Nötzel, ... ArXiv, 2023 | 22 | 2023 |
| Surgical data science: A consensus perspective L Maier-Hein, M Eisenmann, C Feldmann, H Feussner, G Forestier, ... arXiv preprint arXiv:1806.03184, 2018 | 19 | 2018 |
| Common limitations of image processing metrics: A picture story. arXiv 2021 A Reinke, M Eisenmann, MD Tizabi, CH Sudre, T Rädsch, M Antonelli, ... arXiv preprint arXiv:2104.05642, 0 | 18 | |
| Why is the winner the best? M Eisenmann, A Reinke, V Weru, MD Tizabi, F Isensee, TJ Adler, S Ali, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 17 | 2023 |
| Common pitfalls and recommendations for grand challenges in medical artificial intelligence A Reinke, MD Tizabi, M Eisenmann, L Maier-Hein European Urology Focus 7 (4), 710-712, 2021 | 17 | 2021 |
