Institution
Cairo University
Education•Giza, Egypt•
About: Cairo University is a education organization based out in Giza, Egypt. It is known for research contribution in the topics: Population & Medicine. The organization has 33532 authors who have published 55581 publications receiving 792654 citations. The organization is also known as: Fuad I University & King Fuad I University.
Topics: Population, Medicine, Cancer, Breast cancer, Diabetes mellitus
Papers published on a yearly basis
Papers
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Jeffrey D. Stanaway1, Ashkan Afshin1, Emmanuela Gakidou1, Stephen S Lim1 +1050 more•Institutions (346)
TL;DR: This study estimated levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs) by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017 and explored the relationship between development and risk exposure.
2,910 citations
TL;DR: In this article, the authors used a Bayesian hierarchical model to estimate trends in diabetes prevalence, defined as fasting plasma glucose of 7.0 mmol/L or higher, or history of diagnosis with diabetes, or use of insulin or oral hypoglycaemic drugs in 200 countries and territories in 21 regions, by sex and from 1980 to 2014.
2,782 citations
Gregory A. Roth1, Catherine O. Johnson1, Amanuel Alemu Abajobir2, Foad Abd-Allah3 +170 more•Institutions (99)
TL;DR: The GBD (Global Burden of Disease) 2015 study integrated data on disease incidence, prevalence, and mortality to produce consistent, up-to-date estimates for cardiovascular burden, finding that CVDs remain a major cause of health loss for all regions of the world.
2,525 citations
01 Jan 2003
TL;DR: In this article, it is shown that the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time.
Abstract: Conventionally, time series have been studied either in the time domain or the frequency domain. The representation of a signal in the time domain is localized in time, i.e. the value of the signal at each instant in time is well defined. However, the time representation of a signal is poorly localized in frequency, i.e. little information about the frequency content of the signal at a certain frequency can be known by looking at the signal in the time domain. On the other hand, the representation of a signal in the frequency domain is well localized in frequency, but is poorly localized in time, and as a consequence it is impossible to tell when certain events occurred in time.
2,317 citations
TL;DR: A novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic and the neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings.
Abstract: We present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
1,786 citations
Authors
Showing all 33886 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Chiara Mariotti | 141 | 1426 | 98157 |
| Pierluigi Paolucci | 138 | 1965 | 105050 |
| Andrea Giammanco | 135 | 1362 | 98093 |
| Matthew Herndon | 133 | 1732 | 97466 |
| Eduardo De Moraes Gregores | 133 | 1454 | 92464 |
| Pedro G Mercadante | 129 | 1331 | 86378 |
| Alexander Nikitenko | 129 | 1159 | 82102 |
| Stephen G. Ellis | 127 | 655 | 65073 |
| Peter R. Carroll | 125 | 966 | 64032 |
| Mikhail Dubinin | 125 | 1091 | 79808 |
| Cesar Augusto Bernardes | 124 | 965 | 70889 |
| K. Krajczar | 124 | 646 | 65885 |
| Flavia De Almeida Dias | 120 | 590 | 59083 |
| Jaap Goudsmit | 111 | 581 | 42149 |
| Hans J. Eysenck | 106 | 512 | 59690 |