Carlos A. Silva
University of Florida
831 Papers
3.5K Citations
Carlos A. Silva is an academic researcher from University of Florida. The author has contributed to research in topics: ISTTOK & Tokamak. The author has an hindex of 55, co-authored 765 publications. Previous affiliations of Carlos A. Silva include Goddard Space Flight Center & Universidade Federal de Santa Catarina.
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Papers
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze,Andras Jakab,Stefan Bauer,Jayashree Kalpathy-Cramer,Keyvan Farahani,Justin Kirby,Yuliya Burren,N Porz,Johannes Slotboom,Roland Wiest,Levente Lanczi,Elizabeth R. Gerstner,Marc-André Weber,Tal Arbel,Brian B. Avants,Nicholas Ayache,Patricia Buendia,D. Louis Collins,Nicolas Cordier,Jason J. Corso,Antonio Criminisi,Tilak Das,Hervé Delingette,Çağatay Demiralp,Christopher R. Durst,Michel Dojat,Senan Doyle,Joana Festa,Florence Forbes,Ezequiel Geremia,Ben Glocker,Polina Golland,Xiaotao Guo,Andac Hamamci,Khan M. Iftekharuddin,Raj Jena,Nigel M. John,Ender Konukoglu,Danial Lashkari,José Mariz,Raphael Meier,Sérgio Pereira,Doina Precup,Stephen J. Price,Tammy Riklin Raviv,Syed M. S. Reza,Michael Ryan,Duygu Sarikaya,Lawrence H. Schwartz,Hoo-Chang Shin,Jamie Shotton,Carlos A. Silva,Nuno Sousa,Nagesh K. Subbanna,Gábor Székely,Thomas J. Taylor,Owen M. Thomas,Nicholas J. Tustison,Gozde Unal,Flor Vasseur,Max Wintermark,Dong Hye Ye,Liang Zhao,Binsheng Zhao,Darko Zikic,Marcel Prastawa,Mauricio Reyes,Koen Van Leemput +67 more
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
TL;DR: This paper proposes an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels, which allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network.
2.5K
The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography
Ralph Dubayah,J. B. Blair,Scott J. Goetz,Lola Fatoyinbo,Matthew C. Hansen,Sean P. Healey,Michelle Hofton,George C. Hurtt,James R. Kellner,Scott B. Luthcke,John Armston,Hao Tang,Laura Duncanson,Steven Hancock,Patrick Jantz,S. Marselis,Paul L. Patterson,Wenlu Qi,Carlos A. Silva +18 more
- 01 Jun 2020
TL;DR: The Global Ecosystem Dynamics Investigation (GEDI) was launched to the International Space Station in late 2018 to provide high-quality measurements of forest vertical structure in temperate and tropical forests between 51.6° N & S latitude as mentioned in this paper.
Patent
Informing network users of television programming viewed by other network users
Robert M. Cooper,Laurence F. Kirsh,Carlos A. Silva +2 more
- 19 Dec 2000
TL;DR: In this article, a method of informing a first network user of activity by other network users includes receiving information identifying television programming viewed by at least one other network user and displaying the information to the first user on a user interface.
459
On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities
Mauricio Reyes,Raphael Meier,Sérgio Pereira,Carlos A. Silva,Fried-Michael Dahlweid,Hendrik von Tengg-Kobligk,Ronald M. Summers,Roland Wiest +7 more
- 27 May 2020
TL;DR: Insight is provided into the current state of the art of interpretability methods for radiology AI and radiologists' opinions on the topic and suggests trends and challenges that need to be addressed to effectively streamlineinterpretability methods in clinical practice.