His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition (psychology). He connects Machine learning with Data science in his research. His study deals with a combination of Data science and Machine learning. Many of his studies on Segmentation apply to Image segmentation as well. Olaf Ronneberger conducts interdisciplinary study in the fields of Image segmentation and Computer vision through his research. His Computer vision study frequently links to adjacent areas such as Segmentation. Olaf Ronneberger integrates many fields in his works, including Gene and Computational biology. Olaf Ronneberger undertakes multidisciplinary studies into Computational biology and Biochemistry in his work. Olaf Ronneberger combines Biochemistry and Protein structure in his studies.
While working on this project, Olaf Ronneberger studies both Artificial intelligence and Algorithm. His work often combines Algorithm and Artificial intelligence studies. Segmentation is often connected to Image segmentation in his work. His Image segmentation study often links to related topics such as Segmentation. Borrowing concepts from Biochemistry, he weaves in ideas under Gene. His study deals with a combination of Biochemistry and Gene. His research links Image (mathematics) with Computer vision. His Image (mathematics) study frequently draws connections to other fields, such as Computer vision. Machine learning and Deep learning are two areas of study in which he engages in interdisciplinary work.
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger;Philipp Fischer;Thomas Brox.
medical image computing and computer assisted intervention (2015)
Highly accurate protein structure prediction with AlphaFold
John M. Jumper;Richard O. Evans;Alexander Pritzel;Tim Green.
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)
U-Net: deep learning for cell counting, detection, and morphometry
Thorsten Falk;Dominic Mai;Robert Bensch;Özgün Çiçek.
Nature Methods (2019)
Highly accurate protein structure prediction for the human proteome
Kathryn Tunyasuvunakool;Jonas Adler;Zachary Wu;Tim Green.
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)
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)
An objective comparison of cell-tracking algorithms
Vladimír Ulman;Martin Maška;Klas E G Magnusson;Olaf Ronneberger.
Nature Methods (2017)
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