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:
Jan. 21, 2025

Filed:

Mar. 25, 2024
Applicants:

Nantomics, Llc, Culver City, CA (US);

Nanthealth, Inc., Culver City, CA (US);

Inventors:

Bing Song, La Canada, CA (US);

Mustafa Jaber, Los Angeles, CA (US);

Liudmila Beziaeva, Culver City, CA (US);

Shahrooz Rabizadeh, Los Angeles, CA (US);

Assignees:

NantOmics, LLC, Culver City, CA (US);

NantHealth, Inc., Culver City, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06N 3/08 (2023.01); G06T 7/00 (2017.01); G06T 7/194 (2017.01); G06V 10/764 (2022.01); G06V 10/766 (2022.01); G06V 10/77 (2022.01); G06V 10/774 (2022.01); G06V 20/69 (2022.01); G16H 10/40 (2018.01); G16H 30/40 (2018.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06T 7/0014 (2013.01); G06T 7/194 (2017.01); G06V 10/764 (2022.01); G06V 10/766 (2022.01); G06V 10/7715 (2022.01); G06V 10/774 (2022.01); G06V 10/7747 (2022.01); G06V 10/82 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G16H 10/40 (2018.01); G16H 30/40 (2018.01); G06T 2207/10024 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30096 (2013.01);
Abstract

Techniques are provided for determining classifications based on WSIs. A varied-size feature map is generated for each training WSI by generating a grid of patches for the training WSI, segmenting the training WSI into tissue and non-tissue areas, and converting patches comprising the tissue areas into tensors. Bounding boxes are generated based on the patches comprising tissue areas and segmented into feature map patches. A fixed-size feature map is generated based on a subset of the feature map patches. A classifier model is trained to process fixed-size feature maps corresponding to the training WSIs such that, for each fixed-size feature map, the classifier model is operable to assign a WSI-level tissue or cell morphology classification or regression based on the tensors. A classification engine is configured to use the trained classifier model to determine a WSI-level tissue or cell morphology classification or regression for a test WSI.


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