| 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 | 24355 | 2021 |
| nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation F Isensee, PF Jaeger, SAA Kohl, J Petersen, KH Maier-Hein Nature methods 18 (2), 203-211, 2021 | 3996* | 2021 |
| 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 | 2151 | 2021 |
| The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680, 2023 | 1065 | 2023 |
| nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation F Isensee, J Petersen, A Klein, D Zimmerer, PF Jaeger, S Kohl, ... MICCAI 2018, Medical Segmentation Decathlon Challenge Entry, 2018 | 913 | 2018 |
| A Probabilistic U-Net for Segmentation of Ambiguous Images SAA Kohl, B Romera-Paredes, C Meyer, J De Fauw, JR Ledsam, ... Advances in Neural Information Processing Systems (NeurIPS spotlight), 2018 | 581 | 2018 |
| Applying and improving AlphaFold at CASP14 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Proteins: Structure, Function, and Bioinformatics 89 (12), 1711-1721, 2021 | 290 | 2021 |
| Classification of cancer at prostate MRI: deep learning versus clinical PI-RADS assessment P Schelb, S Kohl, JP Radtke, M Wiesenfarth, P Kickingereder, ... Radiology 293 (3), 607-617, 2019 | 285 | 2019 |
| Retina U-Net: Embarrassingly simple exploitation of segmentation supervision for medical object detection PF Jaeger, SAA Kohl, S Bickelhaupt, F Isensee, TA Kuder, HP Schlemmer, ... ML4H Workshop, NeurIPS 2019, 2018 | 234 | 2018 |
| Contrastive training for improved out-of-distribution detection J Winkens, R Bunel, AG Roy, R Stanforth, V Natarajan, JR Ledsam, ... arXiv preprint arXiv:2007.05566, 2020 | 233 | 2020 |
| Radiomic machine learning for characterization of prostate lesions with MRI: comparison to ADC values D Bonekamp, S Kohl, M Wiesenfarth, P Schelb, JP Radtke, M Götz, ... Radiology 289 (1), 128-137, 2018 | 204 | 2018 |
| Adversarial Networks for Prostate Cancer Detection S Kohl, D Bonekamp, HP Schlemmer, K Yaqubi, M Hohenfellner, ... Machine Learning for Health workshop, NIPS 2017, 2017 | 160* | 2017 |
| High Accuracy Protein Structure Prediction Using Deep Learning J Jumper, R Evans, A Pritzel, T Green, M Figurnov, K Tunyasuvunakool, ... Fourteenth Critical Assessment of Techniques for Protein Structure Prediction, 2020 | 155 | 2020 |
| Unsupervised Anomaly Localization using Variational Auto-Encoders D Zimmerer, F Isensee, J Petersen, S Kohl, K Maier-Hein MICCAI 2019, 2019 | 154 | 2019 |
| Context-encoding variational autoencoder for unsupervised anomaly detection D Zimmerer, SAA Kohl, J Petersen, F Isensee, KH Maier-Hein MIDL 2019, Conference Abstract, 2018 | 142 | 2018 |
| Computational predictions of protein structures associated with COVID-19 J Jumper, K Tunyasuvunakool, P Kohli, D Hassabis, AF Team DeepMind Website, 2020 | 104* | 2020 |
| A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities SAA Kohl, B Romera-Paredes, KH Maier-Hein, DJ Rezende, SM Eslami, ... Medical Imaging meets NeurIPS Workshop, NeurIPS 2019, 2019 | 80 | 2019 |
| batchgenerators—a python framework for data augmentation F Isensee, P Jäger, J Wasserthal, D Zimmerer, J Petersen, S Kohl, ... Zenodo 3632567, 2020 | 59* | 2020 |
| This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. A. Cowie, B. Romera-Paredes, S. Nikolov, R. Jain, J J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M …, 2021 | 54 | 2021 |
| Augustin ˇZıdek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon AA Kohl, Andrew J J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... | 39 | 2021 |
Klaus H. Maier-HeinProfessor, Medical Image Computing, German Cancer Research Center在 dkfz.de 的电子邮件经过验证
