| 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 | 362* | 2021 |
| 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 | 305 | 2018 |
| 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 | 290 | 2021 |
| 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), 2018 | 226 | 2018 |
| 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 | 106 | 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 | 101* | 2017 |
| 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 | 100 | 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 | 81 | 2018 |
| 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 | 74 | 2020 |
| 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 | 72 | 2021 |
| Computational predictions of protein structures associated with COVID-19 J Jumper, K Tunyasuvunakool, P Kohli, D Hassabis, AF Team DeepMind Website, 2020 | 56* | 2020 |
| Context-encoding variational autoencoder for unsupervised anomaly detection D Zimmerer, SAA Kohl, J Petersen, F Isensee, KH Maier-Hein MIDL 2019, Conference Abstract, 2018 | 45 | 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 | 43 | 2020 |
| Unsupervised Anomaly Localization using Variational Auto-Encoders D Zimmerer, F Isensee, J Petersen, S Kohl, K Maier-Hein MICCAI 2019, 2019 | 31 | 2019 |
| 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 | 23 | 2019 |
| Deep Probabilistic Modeling of Glioma Growth J Petersen, PF Jäger, F Isensee, SAA Kohl, U Neuberger, W Wick, ... MICCAI 2019, 2019 | 16 | 2019 |
| Shallow‐impurity‐related binding energy and linear optical absorption in ring‐shaped quantum dots and quantum‐well wires under applied electric field SAA Kohl, RL Restrepo, ME Mora‐Ramos, CA Duque Physica Status Solidi (b) 252 (4), 786-794, 2015 | 11 | 2015 |
| A Case for the Score: Identifying Image Anomalies using Variational Autoencoder Gradients D Zimmerer, J Petersen, SAA Kohl, KH Maier-Hein Medical Imaging meets NeurIPS workshop, NeurIPS 2018, 2018 | 8 | 2018 |
| batchgenerators—a python framework for data augmentation F Isensee, P Jaeger, J Wasserthal, D Zimmerer, J Petersen, S Kohl, ... Zenodo https://doi. org/10.5281/zenodo 3632567, 2020 | 6 | 2020 |
| Applying and improving AlphaFold at CASP14 J Jumper, R Evans, A Pritzel, T Green, M Figurnov, O Ronneberger, ... Proteins: Structure, Function, and Bioinformatics, 2021 | 3 | 2021 |
Klaus H. Maier-Hein (né Fritzsche)Professor, Division of Medical Image Computing, German Cancer Research CenterVerified email at dkfz.de
