| The medical segmentation decathlon M Antonelli, A Reinke, S Bakas, K Farahani, A Kopp-Schneider, ... Nature communications 13 (1), 4128, 2022 | 1116 | 2022 |
| 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 | 361 | 2018 |
| Metrics reloaded: Recommendations for image analysis validation TMD Maier-Hein, Lena, Reinke, Annika, Godau, Patrick Nature Methods, 2022 | 316* | 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 | 216 | 2021 |
| Comparative validation of multi-instance instrument segmentation in endoscopy: results of the ROBUST-MIS 2019 challenge T Roß, A Reinke, PM Full, M Wagner, H Kenngott, M Apitz, H Hempe, ... Medical image analysis 70, 101920, 2021 | 128* | 2021 |
| 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 | 116 | 2020 |
| The federated tumor segmentation (fets) challenge S Pati, U Baid, M Zenk, B Edwards, M Sheller, GA Reina, P Foley, ... arXiv preprint arXiv:2105.05874, 2021 | 102 | 2021 |
| 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 | 102 | 2021 |
| Understanding metric-related pitfalls in image analysis validation A Reinke, MD Tizabi, M Baumgartner, M Eisenmann, D Heckmann-Nötzel, ... Nature methods 21 (2), 182-194, 2024 | 96 | 2024 |
| 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 | 92 | 2021 |
| Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the heichole benchmark M Wagner, BP Müller-Stich, A Kisilenko, D Tran, P Heger, L Mündermann, ... Medical Image Analysis 86, 102770, 2023 | 75 | 2023 |
| Labelling instructions matter in biomedical image analysis T Rädsch, A Reinke, V Weru, MD Tizabi, N Schreck, AE Kavur, ... Nature Machine Intelligence 5 (3), 273-283, 2023 | 61 | 2023 |
| Mood 2020: A public benchmark for out-of-distribution detection and localization on medical images D Zimmerer, PM Full, F Isensee, P Jäger, T Adler, J Petersen, G Köhler, ... IEEE transactions on medical imaging 41 (10), 2728-2738, 2022 | 53 | 2022 |
| Semantic segmentation of multispectral photoacoustic images using deep learning M Schellenberg, KK Dreher, N Holzwarth, F Isensee, A Reinke, N Schreck, ... Photoacoustics 26, 100341, 2022 | 52 | 2022 |
| How to exploit weaknesses in biomedical challenge design and organization A Reinke, M Eisenmann, S Onogur, M Stankovic, P Scholz, PM Full, ... International Conference on Medical Image Computing and Computer-Assisted …, 2018 | 45 | 2018 |
| 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 | 33 | 2022 |
| 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 | 26 | 2023 |
| Common limitations of image processing metrics: A picture story. arXiv 2021 A Reinke, MD Tizabi, CH Sudre, M Eisenmann, T Rädsch, M Baumgartner, ... arXiv preprint arXiv:2104.05642, 0 | 26 | |
| 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 | 21 | 2021 |
| Common limitations of performance metrics in biomedical image analysis A Reinke, L Maier-Hein, H Müller Proceedings of the Medical Imaging with Deep Learning (MIDL 2021), 2021 | 21 | 2021 |