Google Inc.

Verily Life Sciences LLC

Yun Liu

Martin Christian Stumpe

Marc Adlai Coram

Philip Charles Nelson

Dale R Webster

Lily Hao Yi Peng

Lily Peng

Ryan Poplin

Avinash Vaidyanathan Varadarajan

Arunachalam Narayanaswamy

Varun Gulshan

Derek Janme Wu

Katharine Blumer

Krishna Kumar Gadepalli

Matthew Symonds

Timo Kohlberger

Kang Li

David Coz

Kunal Nagpal

Davis Foote

Robert Carter Dunn

Craig Mermel

David F Steiner

Ellery Wulczyn

Ayush Jain

Clara Eng

David Henry Way

Peggy Yen Phuong Bui

Vishakha Gupta

Po-Hsuan Cameron Chen

Dennis Ai

Jessica Gallegos

Yuan Liu

Naren Manoj

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

The present disclosure is directed to a deep learning system for differential diagnoses of skin diseases. In particular, the system performs a method that can include obtaining a plurality of images that respectively depict a portion of a patient's skin. The method can include determining, using a machine-learned skin condition classification model, a plurality of embeddings respectively for the plurality of images. The method can include combining the plurality of embeddings to obtain a unified representation associated with the portion of the patient's skin. The method can include determining, using the machine-learned skin condition classification model, a skin condition classification for the portion of the patients skin, the skin condition classification produced by the machine-learned skin condition classification model by processing the unified representation, wherein the skin condition classification identifies one or more skin conditions selected from a plurality of potential skin conditions.

Deep learning system for differential diagnosis of skin diseases

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing fundus images using fundus image processing machine learning models. One of the methods includes obtaining a model input comprising one or more fundus images, each fundus image being an image of a fundus of an eye of a patient; processing the model input using a fundus image processing machine learning model, wherein the fundus image processing machine learning model is configured to process the model input comprising the one or more fundus image to generate a model output; and processing the model output to generate health analysis data.

Processing fundus images using machine learning models

A method, system and machine for assisting a pathologist in identifying the presence of tumor cells in lymph node tissue is disclosed. The digital image of lymph node tissue at a first magnification (e.g., 40&#xd7;) is subdivided into a multitude of rectangular 'patches.' A likelihood of malignancy score is then determined for each of the patches. The score is obtained by analyzing pixel data from the patch (e.g., pixel data centered on and including the patch) using a computer system programmed as an ensemble of deep neural network pattern recognizers, each operating on different magnification levels of the patch. A representation or 'heatmap' of the slide is generated. Each of the patches is assigned a color or grayscale value in accordance with (1) the likelihood of malignancy score assigned to the patch by the combined outputs of the ensemble of deep neural network pattern recognizers and (2) a code which assigns distinct colors (or grayscale values) to different values of likelihood of malignancy scores assigned to the patches.

Method and system for assisting pathologist identification of tumor cells in magnified tissue images

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Mountain View, CA, United States of America

Yun Liu