His primary areas of investigation include Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Data set. His research in the fields of Deep learning and Normalization overlaps with other disciplines such as Set. His Deep learning study deals with Artificial neural network intersecting with Image translation, Brain segmentation, Image segmentation and Biomedical image.
In his papers, Olaf Ronneberger integrates diverse fields, such as Segmentation and Context. His work on Tracking and Image stitching as part of general Computer vision research is frequently linked to High resolution and Software, bridging the gap between disciplines. His research in Data set intersects with topics in Annotation, Structure and Volumetric segmentation.
Olaf Ronneberger mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Segmentation and Cell biology. His Voxel, Support vector machine and Deep learning study in the realm of Artificial intelligence connects with subjects such as Object detection. His study in Computer vision is interdisciplinary in nature, drawing from both Deconvolution and Detector.
His work on Convolutional neural network as part of general Pattern recognition research is frequently linked to Volume, thereby connecting diverse disciplines of science. His research integrates issues of Artificial neural network and Probabilistic logic in his study of Segmentation. His Invariant research incorporates themes from Spherical harmonics and Grayscale.
Olaf Ronneberger spends much of his time researching Artificial intelligence, Segmentation, Pattern recognition, Deep learning and Computer vision. Many of his research projects under Artificial intelligence are closely connected to Task and Set with Task and Set, tying the diverse disciplines of science together. His work carried out in the field of Segmentation brings together such families of science as Colorectal adenocarcinoma, Artificial neural network, Pixel, Probabilistic logic and Radiation therapy.
He combines subjects such as Annotation and Structure with his study of Pattern recognition. Olaf Ronneberger has included themes like Medical physics, Information retrieval and Optical coherence tomography in his Deep learning study. His Visual recognition study in the realm of Computer vision interacts with subjects such as Path, Architecture and Trajectory.
His primary areas of investigation include Artificial intelligence, Pattern recognition, Data set, Volumetric segmentation and Structure. Olaf Ronneberger studies Artificial intelligence, namely Deep learning. His Deep learning research includes elements of Artificial neural network and Biomedical image.
The study incorporates disciplines such as Annotation, Training set and Convolutional neural network in addition to Data set. The subject of his Volumetric segmentation research is within the realm of Computer vision. His study on Image segmentation is often connected to Context as part of broader study in Segmentation.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger;Philipp Fischer;Thomas Brox.
medical image computing and computer assisted intervention (2015)
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
Özgün Çiçek;Ahmed Abdulkadir;Ahmed Abdulkadir;Soeren S. Lienkamp;Thomas Brox.
medical image computing and computer assisted intervention (2016)
Clinically applicable deep learning for diagnosis and referral in retinal disease
Jeffrey De Fauw;Joseph R. Ledsam;Bernardino Romera-Paredes;Stanislav Nikolov.
Nature Medicine (2018)
Highly accurate protein structure prediction with AlphaFold
John M. Jumper;Richard O. Evans;Alexander Pritzel;Tim Green.
Chemotaxonomic Identification of Single Bacteria by Micro-Raman Spectroscopy: Application to Clean-Room-Relevant Biological Contaminations
Petra Rösch;Michaela Harz;Michael Schmitt;Klaus-Dieter Peschke.
Applied and Environmental Microbiology (2005)
A new fate mapping system reveals context-dependent random or clonal expansion of microglia
Tuan Leng Tay;Dominic Mai;Jana Dautzenberg;Francisco Fernández-Klett.
Nature Neuroscience (2017)
A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Amber L. Simpson;Michela Antonelli;Spyridon Bakas;Michel Bilello.
arXiv: Computer Vision and Pattern Recognition (2019)
Gland segmentation in colon histology images: The GlaS challenge contest
Korsuk Sirinukunwattana;Josien P.W. Pluim;Hao Chen;Xiaojuan Qi.
Medical Image Analysis (2017)
Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge
Esther E. Bron;Marion Smits;Wiesje M. van der Flier;Hugo Vrenken.
An objective comparison of cell-tracking algorithms
Vladimír Ulman;Martin Maška;Klas E G Magnusson;Olaf Ronneberger.
Nature Methods (2017)
Profile was last updated on December 6th, 2021.
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