5 code implementations • Nature 2021 • John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, Demis Hassabis
Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.
no code implementations • Nature 2020 • Fabian Isensee, Paul F. Jaeger, Simon A. A. Kohl, Jens Petersen & Klaus H. Maier-Hein
Biomedical imaging is a driver of scientific discovery and a core component of medical care and is being stimulated by the field of deep learning.
no code implementations • 28 Nov 2019 • David Zimmerer, Jens Petersen, Simon A. A. Kohl, Klaus H. Maier-Hein
Through training on unlabeled data, anomaly detection has the potential to impact computer-aided diagnosis by outlining suspicious regions.
2 code implementations • 22 Jul 2019 • Gregor N. Ramien, Paul F. Jaeger, Simon A. A. Kohl, Klaus H. Maier-Hein
To this end, we propose Reg R-CNN, which replaces the second-stage classification model of a current object detector with a regression model.
1 code implementation • 9 Jul 2019 • Jens Petersen, Paul F. Jäger, Fabian Isensee, Simon A. A. Kohl, Ulf Neuberger, Wolfgang Wick, Jürgen Debus, Sabine Heiland, Martin Bendszus, Philipp Kickingereder, Klaus H. Maier-Hein
Existing approaches to modeling the dynamics of brain tumor growth, specifically glioma, employ biologically inspired models of cell diffusion, using image data to estimate the associated parameters.
4 code implementations • 30 May 2019 • Simon A. A. Kohl, Bernardino Romera-Paredes, Klaus H. Maier-Hein, Danilo Jimenez Rezende, S. M. Ali Eslami, Pushmeet Kohli, Andrew Zisserman, Olaf Ronneberger
Medical imaging only indirectly measures the molecular identity of the tissue within each voxel, which often produces only ambiguous image evidence for target measures of interest, like semantic segmentation.
4 code implementations • 17 Apr 2019 • Fabian Isensee, Paul F. Jäger, Simon A. A. Kohl, Jens Petersen, Klaus H. Maier-Hein
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.
no code implementations • 14 Dec 2018 • David Zimmerer, Simon A. A. Kohl, Jens Petersen, Fabian Isensee, Klaus H. Maier-Hein
Unsupervised learning can leverage large-scale data sources without the need for annotations.
6 code implementations • 21 Nov 2018 • Paul F. Jaeger, Simon A. A. Kohl, Sebastian Bickelhaupt, Fabian Isensee, Tristan Anselm Kuder, Heinz-Peter Schlemmer, Klaus H. Maier-Hein
The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors.
8 code implementations • NeurIPS 2018 • Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses.