Institution
University of Toronto
Education•Toronto, Ontario, Canada•
About: University of Toronto is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Health care. The organization has 126067 authors who have published 294940 publications receiving 13536856 citations. The organization is also known as: UToronto & U of T.
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Radboud University Nijmegen1, Eindhoven University of Technology2, Utrecht University3, Beth Israel Deaconess Medical Center4, Massachusetts Institute of Technology5, Harvard University6, The Chinese University of Hong Kong7, Munich Business School8, Middle East Technical University9, University of Toronto10, University of Warwick11, Coventry Health Care12, Qatar University13, HTW Berlin - University of Applied Sciences14, Tampere University of Technology15, University of Tampere16, Technische Universität München17, Osaka University18, University of South Florida19, National Academy of Sciences of Belarus20, University of Castilla–La Mancha21, Pierre-and-Marie-Curie University22, Pontifical Catholic University of Peru23
TL;DR: In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints.
Abstract: Importance Application of deep learning algorithms to whole-slide pathology images can potentially improve diagnostic accuracy and efficiency. Objective Assess the performance of automated deep learning algorithms at detecting metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancer and compare it with pathologists’ diagnoses in a diagnostic setting. Design, Setting, and Participants Researcher challenge competition (CAMELYON16) to develop automated solutions for detecting lymph node metastases (November 2015-November 2016). A training data set of whole-slide images from 2 centers in the Netherlands with (n = 110) and without (n = 160) nodal metastases verified by immunohistochemical staining were provided to challenge participants to build algorithms. Algorithm performance was evaluated in an independent test set of 129 whole-slide images (49 with and 80 without metastases). The same test set of corresponding glass slides was also evaluated by a panel of 11 pathologists with time constraint (WTC) from the Netherlands to ascertain likelihood of nodal metastases for each slide in a flexible 2-hour session, simulating routine pathology workflow, and by 1 pathologist without time constraint (WOTC). Exposures Deep learning algorithms submitted as part of a challenge competition or pathologist interpretation. Main Outcomes and Measures The presence of specific metastatic foci and the absence vs presence of lymph node metastasis in a slide or image using receiver operating characteristic curve analysis. The 11 pathologists participating in the simulation exercise rated their diagnostic confidence as definitely normal, probably normal, equivocal, probably tumor, or definitely tumor. Results The area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.556 to 0.994. The top-performing algorithm achieved a lesion-level, true-positive fraction comparable with that of the pathologist WOTC (72.4% [95% CI, 64.3%-80.4%]) at a mean of 0.0125 false-positives per normal whole-slide image. For the whole-slide image classification task, the best algorithm (AUC, 0.994 [95% CI, 0.983-0.999]) performed significantly better than the pathologists WTC in a diagnostic simulation (mean AUC, 0.810 [range, 0.738-0.884];P Conclusions and Relevance In the setting of a challenge competition, some deep learning algorithms achieved better diagnostic performance than a panel of 11 pathologists participating in a simulation exercise designed to mimic routine pathology workflow; algorithm performance was comparable with an expert pathologist interpreting whole-slide images without time constraints. Whether this approach has clinical utility will require evaluation in a clinical setting.
2,116 citations
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01 May 2000TL;DR: An overview of the field of software systems requirements engineering (RE) is presented, describing the main areas of RE practice, and highlights some key open research issues for the future.
Abstract: This paper presents an overview of the field of software systems requirements engineering (RE). It describes the main areas of RE practice, and highlights some key open research issues for the future.
2,114 citations
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07 Dec 2015
TL;DR: The authors align books to their movie releases to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets, and propose a context-aware CNN to combine information from multiple sources.
Abstract: Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets. To align movies and books we propose a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book. We propose a context-aware CNN to combine information from multiple sources. We demonstrate good quantitative performance for movie/book alignment and show several qualitative examples that showcase the diversity of tasks our model can be used for.
2,105 citations
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TL;DR: This work investigated the mechanism by which transferrin-coated gold nanoparticles (Au NP) of different sizes and shapes entered mammalian cells and developed a mathematical equation to predict the relationship of size versus exocytosis for different cell lines.
Abstract: We investigated the mechanism by which transferrin-coated gold nanoparticles (Au NP) of different sizes and shapes entered mammalian cells. We determined that transferrin-coated Au NP entered the cells via clathrin-mediated endocytosis pathway. The NPs exocytosed out of the cells in a linear relationship to size. This was different than the relationship between uptake and size. Furthermore, we developed a mathematical equation to predict the relationship of size versus exocytosis for different cell lines. These studies will provide guidelines for developing NPs for imaging and drug delivery applications, which will require "controlling" NP accumulation rate. These studies will also have implications in determining nanotoxicity.
2,099 citations
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TL;DR: If new particles are gauged by a new U(1) then their electromagnetic charges may be shifted by a calculable amount as mentioned in this paper, which is the case in the case of the current article.
2,095 citations
Authors
Showing all 127245 results
Name | H-index | Papers | Citations |
---|---|---|---|
Gordon H. Guyatt | 231 | 1620 | 228631 |
David J. Hunter | 213 | 1836 | 207050 |
Rakesh K. Jain | 200 | 1467 | 177727 |
Thomas C. Südhof | 191 | 653 | 118007 |
Gordon B. Mills | 187 | 1273 | 186451 |
George Efstathiou | 187 | 637 | 156228 |
John P. A. Ioannidis | 185 | 1311 | 193612 |
Paul M. Thompson | 183 | 2271 | 146736 |
Yusuke Nakamura | 179 | 2076 | 160313 |
Chris Sander | 178 | 713 | 233287 |
David R. Williams | 178 | 2034 | 138789 |
David L. Kaplan | 177 | 1944 | 146082 |
Jasvinder A. Singh | 176 | 2382 | 223370 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
Deborah J. Cook | 173 | 907 | 148928 |