Institution
University of Oxford
Education•Oxford, Oxfordshire, United Kingdom•
About: University of Oxford is a education organization based out in Oxford, Oxfordshire, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 99713 authors who have published 258108 publications receiving 12972806 citations. The organization is also known as: Oxford University & Oxon..
Topics: Population, Context (language use), Galaxy, Politics, Medicine
Papers published on a yearly basis
Papers
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University of Milan1, Instituto Português de Oncologia Francisco Gentil2, Curie Institute3, Leiden University Medical Center4, Vienna General Hospital5, University College London6, Leiden University7, Institut Jules Bordet8, Netherlands Cancer Institute9, Turku University Hospital10, University of Oxford11, University of Mannheim12, Ludwig Maximilian University of Munich13, Helsinki University Central Hospital14, The Royal Marsden NHS Foundation Trust15, Institute of Cancer Research16, University Medical Center Groningen17, Radboud University Nijmegen18, Institut Gustave Roussy19, Tel Aviv Sourasky Medical Center20, University Hospital of Lausanne21, University of Bologna22, University of Turin23, Weston Park Hospital24, Aarhus University Hospital25, Katholieke Universiteit Leuven26, Erasmus University Rotterdam27, Oslo University Hospital28, University College Hospital29, Claude Bernard University Lyon 130
1,150 citations
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TL;DR: This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images, and presents a classification of methodology in terms of use of prior information.
Abstract: This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem
1,150 citations
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TL;DR: It is shown that errors in the UMI sequence are common and network-based methods to account for these errors when identifying PCR duplicates are introduced, demonstrating the value of properly accounting for errors in UMIs.
Abstract: Unique Molecular Identifiers (UMIs) are random oligonucleotide barcodes that are increasingly used in high-throughput sequencing experiments. Through a UMI, identical copies arising from distinct molecules can be distinguished from those arising through PCR amplification of the same molecule. However, bioinformatic methods to leverage the information from UMIs have yet to be formalized. In particular, sequencing errors in the UMI sequence are often ignored or else resolved in an ad hoc manner. We show that errors in the UMI sequence are common and introduce network-based methods to account for these errors when identifying PCR duplicates. Using these methods, we demonstrate improved quantification accuracy both under simulated conditions and real iCLIP and single-cell RNA-seq data sets. Reproducibility between iCLIP replicates and single-cell RNA-seq clustering are both improved using our proposed network-based method, demonstrating the value of properly accounting for errors in UMIs. These methods are implemented in the open source UMI-tools software package.
1,147 citations
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1,146 citations
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TL;DR: A method of reliably measuring relative orientation co-occurrence statistics in a rotationally invariant manner is presented, and whether incorporating such information can enhance the classifier’s performance is discussed.
Abstract: We investigate texture classification from single images obtained under unknown viewpoint and illumination. A statistical approach is developed where textures are modelled by the joint probability distribution of filter responses. This distribution is represented by the frequency histogram of filter response cluster centres (textons). Recognition proceeds from single, uncalibrated images and the novelty here is that rotationally invariant filters are used and the filter response space is low dimensional.
1,145 citations
Authors
Showing all 101421 results
Name | H-index | Papers | Citations |
---|---|---|---|
Eric S. Lander | 301 | 826 | 525976 |
Albert Hofman | 267 | 2530 | 321405 |
Douglas G. Altman | 253 | 1001 | 680344 |
Salim Yusuf | 231 | 1439 | 252912 |
George Davey Smith | 224 | 2540 | 248373 |
Yi Chen | 217 | 4342 | 293080 |
David J. Hunter | 213 | 1836 | 207050 |
Nicholas J. Wareham | 212 | 1657 | 204896 |
Christopher J L Murray | 209 | 754 | 310329 |
Cyrus Cooper | 204 | 1869 | 206782 |
Mark J. Daly | 204 | 763 | 304452 |
David Miller | 203 | 2573 | 204840 |
Mark I. McCarthy | 200 | 1028 | 187898 |
Raymond J. Dolan | 196 | 919 | 138540 |
Frank E. Speizer | 193 | 636 | 135891 |