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. 02, 2023

Filed:

Jul. 12, 2022
Applicant:

Paige.ai, Inc., New York, NY (US);

Inventors:

Brandon Rothrock, New York, NY (US);

Christopher Kanan, Rochester, NY (US);

Julian Viret, New York, NY (US);

Thomas Fuchs, New York, NY (US);

Leo Grady, Darien, CT (US);

Assignee:

Paige.AI, Inc., New York, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/136 (2017.01); G06T 7/194 (2017.01); G06T 7/00 (2017.01); G06N 20/00 (2019.01); G06K 9/62 (2022.01);
U.S. Cl.
CPC ...
G06T 7/136 (2017.01); G06K 9/6259 (2013.01); G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G06T 7/194 (2017.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01);
Abstract

Systems and methods are disclosed for receiving one or more electronic slide images associated with a tissue specimen, the tissue specimen being associated with a patient and/or medical case, partitioning a first slide image of the one or more electronic slide images into a plurality of tiles, detecting a plurality of tissue regions of the first slide image and/or plurality of tiles to generate a tissue mask, determining whether any of the plurality of tiles corresponds to non-tissue, removing any of the plurality of tiles that are determined to be non-tissue, determining a prediction, using a machine learning prediction model, for at least one label for the one or more electronic slide images, the machine learning prediction model having been generated by processing a plurality of training images, and outputting the prediction of the trained machine learning prediction model.


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