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.
no code implementations • 21 Dec 2018 • Sonia Phene, R. Carter Dunn, Naama Hammel, Yun Liu, Jonathan Krause, Naho Kitade, Mike Schaekermann, Rory Sayres, Derek J. Wu, Ashish Bora, Christopher Semturs, Anita Misra, Abigail E. Huang, Arielle Spitze, Felipe A. Medeiros, April Y. Maa, Monica Gandhi, Greg S. Corrado, Lily Peng, Dale R. Webster
An algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers.
no code implementations • ICLR 2018 • Ashish Bora, Eric Price, Alexandros G. Dimakis
Generative models provide a way to model structure in complex distributions and have been shown to be useful for many tasks of practical interest.
3 code implementations • ICML 2017 • Ashish Bora, Ajil Jalal, Eric Price, Alexandros G. Dimakis
The goal of compressed sensing is to estimate a vector from an underdetermined system of noisy linear measurements, by making use of prior knowledge on the structure of vectors in the relevant domain.
no code implementations • 15 Dec 2016 • Ashish Bora, Sugato Basu, Joydeep Ghosh
Many time series are generated by a set of entities that interact with one another over time.