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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.
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Journal ArticleDOI
An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis
Po-Hsuan Cameron Chen,Krishna Gadepalli,Robert C. MacDonald,Yun Liu,Shiro Kadowaki,Kunal Nagpal,Timo Kohlberger,Jeffrey Dean,Greg S. Corrado,Jason Hipp,Jason Hipp,Craig H. Mermel,Martin C. Stumpe +12 more
TL;DR: The augmented reality microscope (ARM) overlays AI-based information onto the current view of the sample in real time, enabling seamless integration of AI into routine workflows and will remove barriers towards the use of AI designed to improve the accuracy and efficiency of cancer diagnosis.
Journal ArticleDOI
Nanoscale electronic synapses using phase change devices
Bryan L. Jackson,Bipin Rajendran,Greg S. Corrado,Matthew J. Breitwisch,Geoffrey W. Burr,Roger W. Cheek,Kailash Gopalakrishnan,Simone Raoux,Charles T. Rettner,Alvaro Padilla,A. G. Schrott,R. S. Shenoy,B. N. Kurdi,Chung H. Lam,Dharmendra S. Modha +14 more
TL;DR: These devices, when arranged in a crossbar array architecture, could enable the development of synaptronic systems that approach the density and energy efficiency of the human brain.
Journal ArticleDOI
Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India.
Varun Gulshan,Renu P Rajan,Kasumi Widner,Derek Wu,Peter Wubbels,Tyler Rhodes,Kira Whitehouse,Marc Coram,Greg S. Corrado,Kim Ramasamy,Rajiv Raman,Lily Peng,Dale R. Webster +12 more
TL;DR: This study shows that the automated DR system generalizes to this population of Indian patients in a prospective setting and demonstrates the feasibility of using an automated DR grading system to expand screening programs.
Journal ArticleDOI
Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs.
Sonia Phene,R. Carter Dunn,Naama Hammel,Yun Liu,Jonathan Krause,Naho Kitade,Mike Schaekermann,Rory Sayres,Derek 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 +19 more
TL;DR: A deep learning algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers and maintained good performance on an independent dataset with diagnoses based on a full glaucoma workup.
Journal ArticleDOI
Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program.
Paisan Ruamviboonsuk,Jonathan Krause,Peranut Chotcomwongse,Rory Sayres,Rajiv Raman,Kasumi Widner,Bilson J. L. Campana,Sonia Phene,Kornwipa Hemarat,Mongkol Tadarati,Sukhum Silpa-archa,Jirawut Limwattanayingyong,Chetan Rao,Oscar Kuruvilla,Jesse J. Jung,Jeffrey Tan,Surapong Orprayoon,Chawawat Kangwanwongpaisan,Ramase Sukumalpaiboon,Chainarong Luengchaichawang,Jitumporn Fuangkaew,Pipat Kongsap,Lamyong Chualinpha,Sarawuth Saree,Srirut Kawinpanitan,Korntip Mitvongsa,Siriporn Lawanasakol,Chaiyasit Thepchatri,Lalita Wongpichedchai,Greg S. Corrado,Lily Peng,Dale R. Webster +31 more
TL;DR: Across different severity levels of DR for determining referable disease, deep learning significantly reduced the false negative rate at the cost of slightly higher false positive rates (2%).