Google Inc.

Verily Life Sciences LLC

Matthew Symonds

Joy Aloysius Thomas

Kenneth Chan

Alex David Lester

Michael Christopher Yara

Terrence Li

Sibo Liu

Andrew Marsh Gardiner

Che Liu

Sri Harsha Vardhan Reddy Chevuru

Tsenguun Tsogbadrakh

Jared Scott Borner

Kunal Nagpal

Davis Foote

Martin Christian Stumpe

Lily Peng

Yun Liu

Carrie Cai

Craig Mermel

Dave Steiner

Michael Terry

Andrei Kapishnikov

Jimbo Wilson

Ben Wedin

Pan-Pan Jiang

Adam Pearce

Samantha Winter

Cameron Chen

Rory Sayres

Ellery Wulczyn

Naren Manoj

Po-Hsuan Cameron Chen

David F Steiner

Liron Yatziv

One example method includes receiving a digital image of a needle core prostate biopsy, displaying, using a display device, a magnified portion of the digital image, obtaining, from a deep learning model, Gleason scores corresponding to patches of the magnified portion of the digital image, and displaying, using the display device, a superimposed overlay on the magnified portion of the digital image based on the Gleason scores and corresponding confidence values of the Gleason scores, the superimposed overlay comprising one or more outlines corresponding one or more Gleason scores associated with the magnified portion of the digital image and comprising image patches having colors based on a Gleason score of the Gleason scores corresponding to an underlying portion of the magnified portion of the digital image and a confidence value of the corresponding Gleason score.

Artificial intelligence-based assistant for concurrent review of needle core prostate biopsies

One example method for AI prediction of prostate cancer outcomes involves receiving an image of prostate tissue; assigning Gleason pattern values to one or more regions within the image using an artificial intelligence Gleason grading model, the model trained to identify Gleason patterns on a patch-by-patch basis in a prostate tissue image; determining relative areal proportions of the Gleason patterns within the image; assigning at least one of a risk score or risk group value to the image based on the determined relative areal proportions; and outputting at least one of the risk score or the risk group value.

Artificial intelligence prediction of prostate cancer outcomes

Methods and systems that facilitate automatic generation of Application Programming Interface (API) specification from web traffic. Methods include obtaining a plurality of API requests and responses to the plurality of API requests. Methods include processing these API requests and responses to API requests to identify one or more attributes, such as, for example, variables, query parameters, response status codes, and response schemas. Methods include identifying variables using a tree data structure to represent resource paths. Methods include identifying query parameters based on resource items in resource paths. Methods include determining that the API call does not conform to the API specification by comparing one or more attributes of the API call with the attributes of the API specification.

API specification generation

Growing community of inventors

Mountain View, CA, United States of America

Matthew Symonds