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Greg S. Corrado
Researcher at Google
Publications - 149
Citations - 114561
Greg S. Corrado is an academic researcher from Google. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 54, co-authored 125 publications receiving 95930 citations. Previous affiliations of Greg S. Corrado include IBM & Howard Hughes Medical Institute.
Papers
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Journal ArticleDOI
Predicting the risk of developing diabetic retinopathy using deep learning
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 +15 more
TL;DR: In this article, two versions of a deep-learning system were used to predict the development of diabetic retinopathy in patients with diabetes who had had teleretinal DRS screening in a primary care setting.
Journal ArticleDOI
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning.
Avinash V. Varadarajan,Pinal Bavishi,Paisan Ruamviboonsuk,Peranut Chotcomwongse,Subhashini Venugopalan,Arunachalam Narayanaswamy,Jorge Cuadros,Kuniyoshi Kanai,George H. Bresnick,Mongkol Tadarati,Sukhum Silpa-archa,Jirawut Limwattanayingyong,Variya Nganthavee,Joseph R. Ledsam,Pearse A. Keane,Greg S. Corrado,Lily Peng,Dale R. Webster +17 more
TL;DR: A deep learning model is presented that can predict the presence of diabetic macular edema from color fundus photographs with superior specificity and positive predictive value compared to retinal specialists.
Journal ArticleDOI
Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration.
Po-Hsuan Cameron Chen,Krishna Gadepalli,Robert C. MacDonald,Yun Liu,Kunal Nagpal,Timo Kohlberger,Jeffrey Dean,Greg S. Corrado,Jason D. Hipp,Martin C. Stumpe +9 more
TL;DR: The Augmented Reality Microscope (ARM) as mentioned in this paper is a cost-effective solution to the integration of AI, which overlays AI-based information onto the current view of the sample through the optical pathway in real-time, enabling seamless integration of the AI into the regular microscopy workflow.
Journal ArticleDOI
Interpretable survival prediction for colorectal cancer using deep learning
Ellery Wulczyn,David F. Steiner,Melissa Moran,Markus Plass,Robert Reihs,Fraser Tan,Isabelle Flament-Auvigne,Trissia Brown,Peter Regitnig,Po-Hsuan Cameron Chen,Narayan Hegde,Apaar Sadhwani,Robert C. MacDonald,Benny Ayalew,Greg S. Corrado,Lily Peng,Daniel Tse,Heimo Müller,Zhaoyang Xu,Yun Liu,Martin C. Stumpe,Kurt Zatloukal,Craig H. Mermel +22 more
TL;DR: In this article, a deep learning system was developed for predicting disease-specific survival for stage II and III colorectal cancer using 3652 cases (27,300 slides).