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公开(公告)号：US12040080B2
公开(公告)日：2024-07-16
申请号：US17620445
申请日：2020-09-11
Applicant: Google LLC
Inventor： Robert Carter Dunn , Ayush Jain , Peggy Yen Phuong Bui , Clara Eng , David Henry Way , Kang Li , Vishakha Gupta , Jessica Gallegos , Dennis Ai , Yun Liu , David Coz , Yuan Liu
Abstract: 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.
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公开(公告)号：US11636601B2
公开(公告)日：2023-04-25
申请号：US17212811
申请日：2021-03-25
Applicant: GOOGLE LLC
Inventor： Lily Hao Yi Peng , Dale R. Webster , Philip Charles Nelson , Varun Gulshan , Marc Adlai Coram , Martin Christian Stumpe , Derek Janme Wu , Arunachalam Narayanaswamy , Avinash Vaidyanathan Varadarajan , Katharine Blumer , Yun Liu , Ryan Poplin
Abstract: 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.
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3.发明申请公开(公告)号：US20200066407A1
公开(公告)日：2020-02-27
申请号：US16488029
申请日：2017-02-23
Applicant: Google LLC
Inventor： Martin Christian Stumpe , Lily Peng , Yun Liu , Krishna K. Gadepalli , Timo Kohlberger
Abstract: 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×) 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.
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公开(公告)号：US20250117893A1
公开(公告)日：2025-04-10
申请号：US18908549
申请日：2024-10-07
Applicant: Google LLC
Inventor： David Steiner , Ellery Alyosha Wulczyn , Po-Hsuan Chen , Ronnachai Jaroensri , Supriya Vijay , Jeremy Lai , Saloni Agarwal , Yun Liu , Faruk Ahmed
Abstract: An example computer-implemented method for self-supervised training of an image processing model for histopathology images is provided. The example method includes obtaining a reference histopathology image; generating an augmented histopathology image, wherein generating the augmented histopathology image comprises performing, for an input image, at least one of the following augmentations: applying a blur to the input image and injecting noise artifacts into the blurred input image; or cropping a plurality of portions from the input image, wherein the plurality of portions are determined based on a minimum overlap criterion that has been updated over one or more iterations; and training the image processing model based on a similarity of latent representations generated by the image processing model respectively for the reference histopathology image and the augmented histopathology image.
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公开(公告)号：US20230277069A1
公开(公告)日：2023-09-07
申请号：US18011899
申请日：2022-03-03
Applicant: Google LLC
Inventor： Jiening Zhan , Sean Kyungmok Bae , Silviu Borac , Yunus Emre , Jonathan Wesor Wang , Jiang Wu , Mehr Kashyap , Ming Jack Po , Liwen Chen , Melissa Chung , John Cannon , Eric Steven Teasley , James Alexander Taylor, Jr. , Michael Vincent McConnell , Alejandra Maciel , Allen KC Chai , Shwetak Patel , Gregory Sean Corrado , Si-Hyuck Kang , Yun Liu , Michael Rubinstein , Michael Spencer Krainin , Neal Wadhwa
IPC: A61B5/0205 , A61B5/00
CPC classification number: A61B5/0205 , A61B5/0077 , A61B5/725 , A61B5/6898 , A61B5/7257 , A61B5/7278 , A61B5/7485 , A61B5/0816
Abstract: Generally, the present disclosure is directed to systems and methods for measuring heart rate and respiratory rate using a camera such as, for example, a smartphone camera or other consumer-grade camera. Specifically, the present disclosure presents and validates two algorithms that make use of smartphone cameras (or the like) for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. As an example, HR can be measured by placing the finger of a subject over the rear-facing camera. As another example, RR can be measured via a video of the subject sitting still in front of the front-facing camera.
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Patent Agency Ranking
6.发明授权公开(公告)号：US11170897B2
公开(公告)日：2021-11-09
申请号：US16488029
申请日：2017-02-23
Applicant: Google LLC
Inventor： Martin Christian Stumpe , Lily Peng , Yun Liu , Krishna K. Gadepalli , Timo Kohlberger
Abstract: 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×) 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.
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公开(公告)号：US20210209762A1
公开(公告)日：2021-07-08
申请号：US17212811
申请日：2021-03-25
Applicant: GOOGLE LLC
Inventor： Lily Hao Yi Peng , Dale R. Webster , Philip Charles Nelson , Varun Gulshan , Marc Adlai Coram , Martin Christian Stumpe , Derek Janme Wu , Arunachalam Narayanaswamy , Avinash Vaidyanathan Varadarajan , Katharine Blumer , Yun Liu , Ryan Poplin
Abstract: 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.
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公开(公告)号：US20190180441A1
公开(公告)日：2019-06-13
申请号：US16325580
申请日：2017-08-18
Applicant: GOOGLE LLC
Inventor： Lily Hao Yi Peng , Dale R. Webster , Philip Charles Nelson , Varun Gulshan , Marc Adlai Coram , Martin Christian Stumpe , Derek Janme Wu , Arunachalam Narayanaswamy , Avinash Vaidyanathan Varadarajan , Katharine Blumer , Yun Liu , Ryan Poplin
Abstract: 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.
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公开(公告)号：US20230230232A1
公开(公告)日：2023-07-20
申请号：US18011597
申请日：2021-11-02
Applicant: Google LLC
Inventor： Yun Liu , Naama Hammel , Akinori Mitani , Derek Janme Wu , Ashish Dilipchand Bora , Avinash Vaidyanathan Varadarajan , Boris Alekandrovich Babenko
CPC classification number: G06T7/0012 , G16H50/50 , G16H30/40 , A61B3/1241 , G06T2207/30041 , G06T2207/20081 , G06T2207/20084
Abstract: The present disclosure is directed to systems and methods that leverage machine learning for detection of eye or non-eye (e.g., systemic) diseases from external anterior eye images. In particular, a computing system can include and use one or more machine-learned disease detection models to provide disease predictions for a patient based on external anterior eye images of the patient. Specifically, in some example implementations, a computing system can obtain one or more external images that depict an anterior portion of an eye of a patient. The computing system can process the one or more external images with the one or more machine-learned disease detection models to generate a disease prediction for the patient relative to one or more diseases, including, as examples, diseases which present manifestations in a posterior of the eye (e.g., diabetic retinopathy) or systemic diseases (e.g., poorly controlled diabetes).
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公开(公告)号：US10970841B2
公开(公告)日：2021-04-06
申请号：US16325580
申请日：2017-08-18
Applicant: GOOGLE LLC
Inventor： Lily Hao Yi Peng , Dale R. Webster , Philip Charles Nelson , Varun Gulshan , Marc Adlai Coram , Martin Christian Stumpe , Derek Janme Wu , Arunachalam Narayanaswamy , Avinash Vaidyanathan Varadarajan , Katharine Blumer , Yun Liu , Ryan Poplin
Abstract: 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.
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