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:
Sep. 21, 2021

Filed:

Jul. 11, 2018
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Krishnan Eswaran, San Francisco, CA (US);

Shravya Shetty, San Francisco, CA (US);

Daniel Shing Shun Tse, Mountain View, CA (US);

Shahar Jamshy, Santa Clara, CA (US);

Zvika Ben-Haim, Haifa, IL;

Assignee:

Google LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/00 (2019.01); G06F 7/00 (2006.01); G06F 16/432 (2019.01); G16H 30/40 (2018.01); G16H 30/20 (2018.01); G06K 9/62 (2006.01); G06T 7/00 (2017.01); G06F 17/18 (2006.01);
U.S. Cl.
CPC ...
G06F 16/434 (2019.01); G06F 17/18 (2013.01); G06K 9/6215 (2013.01); G06T 7/0016 (2013.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G06T 2207/10124 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30008 (2013.01);
Abstract

A computer-implemented system is described for identifying and retrieving similar radiology images to a query image. The system includes one or more fetchers receiving the query image and retrieving a set of candidate similar radiology images from a data store. One or more scorers receive the query image and the set of candidate similar radiology images and generate a similarity score between the query image and each candidate image. A pooler receives the similarity scores from the one or more scorers, ranks the candidate images, and returns a list of the candidate images reflecting the ranking. The scorers implement a modelling technique to generate the similarity score capturing a plurality of similarity attributes of the query image and the set of candidate similar radiology images and annotations associated therewith. For example, the similarity attributes could be patient, diagnostic and/or visual similarity, and the modelling techniques could be triplet loss, classification loss, regression loss and object detection loss.


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