P
Pedro Mário Cruz e Silva
Researcher at Nvidia
Publications - 10
Citations - 199
Pedro Mário Cruz e Silva is an academic researcher from Nvidia. The author has contributed to research in topics: Facies & Health care. The author has an hindex of 3, co-authored 8 publications receiving 16 citations.
Papers
More filters
Journal ArticleDOI
Federated learning for predicting clinical outcomes in patients with COVID-19.
Ittai Dayan,Holger R. Roth,Aoxiao Zhong,Ahmed Harouni,Amilcare Gentili,Anas Z. Abidin,Andrew Liu,Anthony Costa,Bradford J. Wood,Chien-Sung Tsai,Chih-Hung Wang,Chun-Nan Hsu,C. K. Lee,Peiying Ruan,Daguang Xu,Dufan Wu,Eddie Huang,Felipe Kitamura,Griffin Lacey,Gustavo César de Antônio Corradi,Gustavo Nino,Hao-Hsin Shin,Hirofumi Obinata,Hui Ren,Jason C. Crane,Jesse Tetreault,Jiahui Guan,John Garrett,Joshua D. Kaggie,Jung Gil Park,Keith J. Dreyer,Krishna Juluru,Kristopher Kersten,Marcio Aloisio Bezerra Cavalcanti Rockenbach,Marius George Linguraru,Marius George Linguraru,Masoom A. Haider,Masoom A. Haider,Meena AbdelMaseeh,Nicola Rieke,Pablo F. Damasceno,Pedro Mário Cruz e Silva,Pochuan Wang,Sheng Xu,Shuichi Kawano,Sira Sriswasdi,Soo-Young Park,Thomas M. Grist,Varun Buch,Watsamon Jantarabenjakul,Watsamon Jantarabenjakul,Weichung Wang,Won Young Tak,Xiang Li,Xihong Lin,Young Joon Kwon,Abood Quraini,Andrew Feng,Andrew N. Priest,Baris Turkbey,Benjamin S. Glicksberg,Bernardo Bizzo,Byung Seok Kim,Carlos Tor-Díez,Chia-Cheng Lee,Chia-Jung Hsu,Chin Lin,Chiu-Ling Lai,Christopher P. Hess,Colin B. Compas,Deepeksha Bhatia,Eric K. Oermann,Evan Leibovitz,Hisashi Sasaki,Hitoshi Mori,Isaac Yang,Jae Ho Sohn,Krishna Nand Keshava Murthy,Li-Chen Fu,Matheus Ribeiro Furtado de Mendonça,Mike Fralick,Min Kyu Kang,Mohammad Adil,Natalie Gangai,Peerapon Vateekul,Pierre Elnajjar,Sarah E Hickman,Sharmila Majumdar,Shelley McLeod,Sheridan Reed,Stefan Gräf,Stephanie Harmon,Tatsuya Kodama,Thanyawee Puthanakit,Thanyawee Puthanakit,Tony Mazzulli,Tony Mazzulli,Vitor Lavor,Yothin Rakvongthai,Yu Rim Lee,Yuhong Wen,Fiona J. Gilbert,Mona Flores,Quanzheng Li +103 more
TL;DR: In this article, the authors used federated learning to predict future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays.
Posted ContentDOI
Federated Learning used for predicting outcomes in SARS-COV-2 patients
Mona Flores,Ittai Dayan,Holger R. Roth,Aoxiao Zhong,Ahmed Harouni,Amilcare Gentili,Anas Z. Abidin,Andrew Liu,Anthony Costa,Bradford J. Wood,Chien-Sung Tsai,Chih-Hung Wang,Chun-Nan Hsu,C. K. Lee,Colleen Ruan,Daguang Xu,Dufan Wu,Eddie Huang,Felipe Kitamura,Griffin Lacey,Gustavo César de Antônio Corradi,Hao-Hsin Shin,Hirofumi Obinata,Hui Ren,Jason C. Crane,Jesse Tetreault,Jiahui Guan,John Garrett,Jung Gil Park,Keith J. Dreyer,Krishna Juluru,Kristopher Kersten,Marcio Aloisio Bezerra Cavalcanti Rockenbach,Marius George Linguraru,Masoom A. Haider,Meena Abdelmaseeh,Nicola Rieke,Pablo F. Damasceno,Pedro Mário Cruz e Silva,Pochuan Wang,Sheng Xu,Shuichi Kawano,Sira Sriswa,Soo-Young Park,Thomas M. Grist,Varun Buch,Watsamon Jantarabenjakul,Weichung Wang,Won Young Tak,Xiang Li,Xihong Lin,Fred Kwon,Fiona J. Gilbert,J. D. Kaggie,Quanzheng Li,Abood Quraini,Andrew Feng,Andrew N. Priest,Baris Turkbey,Benjamin S. Glicksberg,Bernardo Bizzo,Byung Seok Kim,Carlos Tor-Díez,Chia-Cheng Lee,Chia-Jung Hsu,Chin Lin,Chiu-Ling Lai,Christopher P. Hess,Colin B. Compas,Deepi Bhatia,Eric K. Oermann,Evan Leibovitz,Hisashi Sasaki,Hitoshi Mori,Isaac Yang,Jae Ho Sohn,Krishna Nand Keshava Murthy,Li-Chen Fu,Matheus Ribeiro Furtado de Mendonça,Mike Fralick,Min Kyu Kang,Mohammad Adil,Natalie Gangai,Peerapon Vateekul,Pierre Elnajjar,Sarah E Hickman,Sharmila Majumdar,Shelley McLeod,Sheridan Reed,Stefan Gräf,Stephanie Harmon,Tatsuya Kodama,Thanyawee Puthanakit,Tony Mazzulli,Vitor de Lima Lavor,Yothin Rakvongthai,Yu Rim Lee,Yuhong Wen +97 more
TL;DR: In this paper, the authors used federated learning to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model.
Proceedings ArticleDOI
Accelerating Multi-attribute Unsupervised Seismic Facies Analysis With RAPIDS
Otávio Oliveira Napoli,Vanderson Martins do Rosario,João Paulo Navarro,Pedro Mário Cruz e Silva,Edson Borin +4 more
TL;DR: It is shown that the high-performance distributed implementation of the k-means algorithm can be used to classify facies in large seismic datasets much faster than a classical parallel CPU implementation (up to 258-fold faster in NVIDIA V100 GPUs), especially for large seismic blocks.
Book ChapterDOI
Low Bit Rate 2D Seismic Image Compression with Deep Autoencoders
TL;DR: A deep learning approach for very low bit rate seismic data compression that benefits from the intrinsic characteristic of deep learning methods and automatically captures the most relevant features of seismic data.