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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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Detecting Cancer Metastases on Gigapixel Pathology Images
Yun Liu,Krishna Gadepalli,Mohammad Norouzi,George E. Dahl,Timo Kohlberger,Subhashini Venugopalan,Aleksey S Boyko,Aleksei Timofeev,Philip Q Nelson,Greg S. Corrado,Jason D. Hipp,Lily Peng,Martin C. Stumpe +12 more
TL;DR: This work presents a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x100,000 pixels and achieves image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides.
Journal ArticleDOI
Ensuring Fairness in Machine Learning to Advance Health Equity.
TL;DR: The mechanisms by which a model's design, data, and deployment may lead to disparities are described; how different approaches to distributive justice in machine learning can advance health equity are explained; and what contexts are more appropriate for different equity approaches inMachine learning.
Proceedings Article
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
TL;DR: This paper proposed BilBOWA (Bilingual Bag-of-Words without Alignments), a simple and computationally efficient model for learning bilingual distributed representations of words which can scale to large monolingual datasets and does not require word-aligned parallel training data.
Posted Content
BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
TL;DR: It is shown that bilingual embeddings learned using the proposed BilBOWA model outperform state-of-the-art methods on a cross-lingual document classification task as well as a lexical translation task on WMT11 data.
Journal ArticleDOI
Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy
Jonathan Krause,Varun Gulshan,Ehsan Rahimy,Peter Karth,Kasumi Widner,Greg S. Corrado,Lily Peng,Dale R. Webster +7 more
TL;DR: Adjudication reduces the errors in DR grading by using a small number of adjudicated consensus grades as a tuning dataset and higher-resolution images as input, and to train an improved automated algorithm for DR grading.