Следене
Simon Kohl
Simon Kohl
Research Scientist, DeepMind
Потвърден имейл адрес: google.com - Начална страница
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Позовавания
Позовавания
Година
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
70112021
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
1248*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
9172021
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
5412018
The liver tumor segmentation benchmark (lits)
P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ...
Medical Image Analysis, 102680, 2022
4882022
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
3662018
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
1812019
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
1542018
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
1412018
Adversarial Networks for Prostate Cancer Detection
S Kohl, D Bonekamp, HP Schlemmer, K Yaqubi, M Hohenfellner, ...
Machine Learning for Health workshop, NIPS 2017, 2017
129*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
1212020
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
1212020
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
962021
Context-encoding variational autoencoder for unsupervised anomaly detection
D Zimmerer, SAA Kohl, J Petersen, F Isensee, KH Maier-Hein
MIDL 2019, Conference Abstract, 2018
882018
Unsupervised Anomaly Localization using Variational Auto-Encoders
D Zimmerer, F Isensee, J Petersen, S Kohl, K Maier-Hein
MICCAI 2019, 2019
772019
Computational predictions of protein structures associated with COVID-19
J Jumper, K Tunyasuvunakool, P Kohli, D Hassabis, AF Team
DeepMind Website, 2020
68*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
502019
batchgenerators—a python framework for data augmentation
F Isensee, P Jäger, J Wasserthal, D Zimmerer, J Petersen, S Kohl, ...
Zenodo https://doi. org/10.5281/zenodo 3632567, 2020
27*2020
Deep Probabilistic Modeling of Glioma Growth
J Petersen, PF Jäger, F Isensee, SAA Kohl, U Neuberger, W Wick, ...
MICCAI 2019, 2019
252019
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
152018
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