US 12,243,635 B2
Systems and methods to process electronic images for synthetic image generation
Rodrigo Ceballos Lentini, Flemington, NJ (US); and Christopher Kanan, Pittsford, NY (US)
Assigned to Paige.AI, Inc., New York, NY (US)
Filed by PAIGE.AI, Inc., New York, NY (US)
Filed on Dec. 29, 2023, as Appl. No. 18/400,539.
Application 18/400,539 is a continuation of application No. 18/181,630, filed on Mar. 10, 2023, granted, now 11,901,064.
Application 18/181,630 is a continuation of application No. 17/806,519, filed on Jun. 13, 2022, granted, now 11,626,201, issued on Apr. 11, 2023.
Application 17/806,519 is a continuation of application No. 17/645,296, filed on Dec. 20, 2021, granted, now 11,393,575, issued on Jul. 19, 2022.
Application 17/645,296 is a continuation of application No. 17/645,197, filed on Dec. 20, 2021, granted, now 11,393,574, issued on Jul. 19, 2022.
Claims priority of provisional application 63/203,036, filed on Jul. 6, 2021.
Prior Publication US 2024/0145067 A1, May 2, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 30/20 (2018.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01)
CPC G16H 30/20 (2018.01) [G06N 20/00 (2019.01); G06T 7/0012 (2013.01); G16H 30/40 (2018.01); G16H 50/50 (2018.01); G06T 7/11 (2017.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20112 (2013.01); G06T 2207/20212 (2013.01); G06T 2207/30004 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for generating a synthetic medical image using style transfer, the system comprising:
a processor; and
a memory coupled to the processor and storing instructions that, when executed by the processor, cause the system to perform operations comprising:
receiving a target medical image;
receiving a segmentation mask;
receiving one or more source medical images;
using the segmentation mask, dividing the target medical image into one or more tiles;
providing the one or more tiles as input to a trained machine learning system;
receiving, for each of the one or more tiles, gradients associated with a content and a style of the respective tile as output of the trained machine learning system;
altering one or more pixels of at least one of the one or more tiles of the target medical image based on the gradients to maintain content of the target medical image while transferring one or more styles from the one or more source medical images to the target medical image; and
generating the synthetic medical image from the target medical image based on the altering.