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:
Nov. 25, 2025

Filed:

Dec. 31, 2024
Applicant:

Insitro, Inc., South San Francisco, CA (US);

Inventors:

Herve Marie-Nelly, San Francisco, CA (US);

Jeevaa Velayutham, Shah Alam, MY;

Assignee:

Insitro, Inc., South San Francisco, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 20/69 (2022.01); A61B 5/00 (2006.01); A61B 10/00 (2006.01); G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01); G06T 5/60 (2024.01); G06T 7/00 (2017.01); G06T 7/10 (2017.01);
U.S. Cl.
CPC ...
G06V 20/69 (2022.01); G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06T 5/60 (2024.01); G06T 7/0012 (2013.01); G06T 7/10 (2017.01); A61B 5/7267 (2013.01); A61B 10/00 (2013.01); G06T 2207/10056 (2013.01); G06T 2207/10064 (2013.01); G06T 2207/10152 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

Described are systems and methods for training a machine-learning model to generate image of biological samples, and systems and methods for generating enhanced images of biological samples. The method for training a machine-learning model to generate images of biological samples may include obtaining a plurality of training images comprising a training image of a first type, and a training image of a second type. The method may also include generating, based on the training image of the first type, a plurality of wavelet coefficients using the machine-learning model; generating, based on the plurality of wavelet coefficients, a synthetic image of the second type; comparing the synthetic image of the second type with the training image of the second type; and updating the machine-learning model based on the comparison.


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