US 12,332,970 B2
Biological image transformation using machine-learning models
Herve Marie-Nelly, San Francisco, CA (US); and Jeevaa Velayutham, Puchong (MY)
Assigned to Insitro, Inc., South San Francisco, CA (US)
Filed by Insitro, Inc., South San Francisco, CA (US)
Filed on Jul. 18, 2022, as Appl. No. 17/867,537.
Application 17/867,537 is a division of application No. 17/480,047, filed on Sep. 20, 2021, granted, now 11,423,256.
Application 17/480,047 is a continuation of application No. PCT/US2021/049327, filed on Sep. 7, 2021.
Claims priority of provisional application 63/143,707, filed on Jan. 29, 2021.
Claims priority of provisional application 63/075,751, filed on Sep. 8, 2020.
Prior Publication US 2022/0358331 A1, Nov. 10, 2022
Int. Cl. G06F 18/214 (2023.01); A61B 5/00 (2006.01); A61B 10/00 (2006.01); G06F 18/2431 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01); G06T 7/00 (2017.01); G06T 7/10 (2017.01)
CPC G06F 18/214 (2023.01) [G06F 18/2431 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); 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)] 30 Claims
OG exemplary drawing
 
1. A system for evaluating a treatment with respect to a disease of interest, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for:
receiving first one or more images depicting one or more untreated biological samples affected by the disease of interest;
receiving second one or more images depicting one or more treated biological samples affected by the disease of interest and treated by the treatment;
inputting the first one or more images into a trained machine-learning model to obtain first one or more transformed images;
inputting the second one or more images into the trained machine-learning model to obtain second one or more transformed images;
comparing the first one or more transformed images and the second one or more transformed images to evaluate the treatment.