The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 17, 2022

Filed:

Aug. 03, 2020
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Dale M. Ando, South San Francisco, CA (US);

Marc Berndl, Mountain View, CA (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2022.01); G06N 3/08 (2006.01); G06V 30/262 (2022.01); G01N 33/48 (2006.01); G01N 33/20 (2019.01); G06V 20/69 (2022.01);
U.S. Cl.
CPC ...
G06K 9/6267 (2013.01); G06K 9/6215 (2013.01); G06K 9/6228 (2013.01); G06K 9/6257 (2013.01); G06K 9/6288 (2013.01); G06N 3/08 (2013.01); G06V 30/274 (2022.01); G01N 33/20 (2013.01); G01N 33/48 (2013.01); G06V 20/698 (2022.01); G06V 2201/10 (2022.01);
Abstract

The present disclosure relates to phenotype analysis of cellular image data using a deep metric network. One example embodiment includes a method. The method includes receiving a target image of a target biological cell having a target phenotype. The method also includes obtaining a semantic embedding associated with the target image. The semantic embedding is generated using a machine-learned, deep metric network model. Further, the method includes obtaining, for each of a plurality of candidate images of candidate biological cells each having a respective candidate phenotype, a semantic embedding associated with the respective candidate image. In addition, the method includes identifying, for each of the semantic embeddings, common morphological variations and reducing, for each of the semantic embeddings based on the identified common morphological variations, effects of nuisances. Even further, the method includes determining, by the computing device, a similarity score for each candidate image.


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