2 code implementations • 19 May 2022 • Shekoofeh Azizi, Laura Culp, Jan Freyberg, Basil Mustafa, Sebastien Baur, Simon Kornblith, Ting Chen, Patricia MacWilliams, S. Sara Mahdavi, Ellery Wulczyn, Boris Babenko, Megan Wilson, Aaron Loh, Po-Hsuan Cameron Chen, YuAn Liu, Pinal Bavishi, Scott Mayer McKinney, Jim Winkens, Abhijit Guha Roy, Zach Beaver, Fiona Ryan, Justin Krogue, Mozziyar Etemadi, Umesh Telang, Yun Liu, Lily Peng, Greg S. Corrado, Dale R. Webster, David Fleet, Geoffrey Hinton, Neil Houlsby, Alan Karthikesalingam, Mohammad Norouzi, Vivek Natarajan
These results suggest that REMEDIS can significantly accelerate the life-cycle of medical imaging AI development thereby presenting an important step forward for medical imaging AI to deliver broad impact.
no code implementations • 10 Aug 2020 • Ashish Bora, Siva Balasubramanian, Boris Babenko, Sunny Virmani, Subhashini Venugopalan, Akinori Mitani, Guilherme de Oliveira Marinho, Jorge Cuadros, Paisan Ruamviboonsuk, Greg S. Corrado, Lily Peng, Dale R. Webster, Avinash V. Varadarajan, Naama Hammel, Yun Liu, Pinal Bavishi
We created and validated two versions of a deep learning system (DLS) to predict the development of mild-or-worse ("Mild+") DR in diabetic patients undergoing DR screening.
1 code implementation • 10 Jul 2020 • Arunachalam Narayanaswamy, Subhashini Venugopalan, Dale R. Webster, Lily Peng, Greg Corrado, Paisan Ruamviboonsuk, Pinal Bavishi, Rory Sayres, Abigail Huang, Siva Balasubramanian, Michael Brenner, Philip Nelson, Avinash V. Varadarajan
Model explanation techniques play a critical role in understanding the source of a model's performance and making its decisions transparent.
no code implementations • 18 Oct 2018 • Avinash Varadarajan, Pinal Bavishi, Paisan Raumviboonsuk, Peranut Chotcomwongse, Subhashini Venugopalan, Arunachalam Narayanaswamy, Jorge Cuadros, Kuniyoshi Kanai, George Bresnick, Mongkol Tadarati, Sukhum Silpa-archa, Jirawut Limwattanayingyong, Variya Nganthavee, Joe Ledsam, Pearse A. Keane, Greg S. Corrado, Lily Peng, Dale R. Webster
To improve the accuracy of DME screening, we trained a deep learning model to use color fundus photographs to predict ci-DME.