Olaf Ronneberger is a researcher in the field of computer science, specialising in artificial intelligence and its applications in scientific problem-solving. He currently works at Google DeepMind in London, UK, and is affiliated with the University of Freiburg, Germany.
Ronneberger received his education at the University of Freiburg, where he also began his career as an assistant professor in the Department of Computer Science, Faculty of Engineering. He then moved to DeepMind in London, continuing his research in AI and machine learning.
Ronneberger's primary research interests include artificial intelligence, pattern recognition, image processing, and biomedical image analysis. He has made significant contributions in the field of deep learning, particularly in the development of convolutional networks for biomedical image segmentation, known as U-Net. His work has been widely recognised and cited, with his most notable publications being:
"U-Net: Convolutional Networks for Biomedical Image Segmentation" (2015), which has received over 20,000 citations. In this paper, Ronneberger and his co-authors propose a network and training strategy that efficiently utilises annotated samples to train deep networks. The method has been successful in various image segmentation tasks and has been praised for its speed and accuracy.
"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation" (2016), with 1644 citations. This paper introduces a network for volumetric segmentation, capable of learning from sparse annotations.
"Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest" (2017), which addresses the challenge of colorectal adenocarcinoma detection and segmentation in colon histology images.
"Clinically applicable deep learning for diagnosis and referral in retinal disease" (2018), with 811 citations. This work focuses on applying deep learning for the diagnosis and referral of retinal diseases.
Olaf Ronneberger is a researcher in the field of artificial intelligence and computer vision. His work focuses on image segmentation, deep learning, and biomedical image analysis.
Ronneberger is currently based at Google DeepMind in London, UK, and is affiliated with the University of Freiburg, Germany. He previously held positions at the University of Jena and the German Cancer Research Center.
No information found on his education.
Ronneberger's research interests include deep learning architectures, the application of AI to scientific problems, and protein structure prediction. His most notable work is in the development of U-Net, a convolutional neural network for biomedical image segmentation. This network utilizes data augmentation techniques to efficiently use annotated samples and has been applied to various medical imaging tasks, including segmentation of neuronal structures and cell tracking.
Another significant contribution is his involvement in AlphaFold, a project that utilizes deep learning to predict protein structures with high accuracy.
Ronneberger has co-authored numerous publications, including:
Olaf Ronneberger is a researcher in the field of artificial intelligence and computer vision. He is currently affiliated with Google DeepMind in London, UK, and the University of Freiburg, Germany.
Ronneberger received his academic training at the University of Freiburg, where he also worked as an academic researcher. He has previously been affiliated with Google and the University of Jena.
Ronneberger's research interests include artificial intelligence, deep learning, image processing and segmentation, and protein structure prediction. He has contributed to research in the following areas:
Ronneberger has co-authored over 140 publications and has an h-index of 45. His most cited works include:
Ronneberger has frequently collaborated with Thomas Brox, Özgün Çiçek, Bernardino Romera-Paredes, Stanislav Nikolov, and many others.