US 11,810,298 B2
Machine-learned hormone status prediction from image analysis
Nikhil Naik, Palo Alto, CA (US); Ali Madani, Palo Alto, CA (US); and Nitish Shirish Keskar, San Francisco, CA (US)
Assigned to Salesforce, Inc., San Francisco, CA (US)
Filed by Salesforce, Inc., San Francisco, CA (US)
Filed on Oct. 21, 2022, as Appl. No. 17/971,312.
Application 17/971,312 is a continuation of application No. 16/895,983, filed on Jun. 8, 2020, granted, now 11,508,481.
Claims priority of provisional application 62/986,479, filed on Mar. 6, 2020.
Prior Publication US 2023/0042318 A1, Feb. 9, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01); G16H 50/20 (2018.01); G06N 5/04 (2023.01); G16H 10/20 (2018.01); G06N 20/00 (2019.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06V 20/69 (2022.01)
CPC G06T 7/0012 (2013.01) [G06F 18/217 (2023.01); G06F 18/2148 (2023.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06V 20/69 (2022.01); G16H 10/20 (2018.01); G16H 50/20 (2018.01); G06V 2201/03 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method for predicting hormone receptor status using machine learning comprising:
partitioning a test H&E stain image of a test tissue sample into a plurality of non-overlapping image tiles;
sampling a subset of non-overlapping image tiles from the plurality of non-overlapping image tiles of the test H&E stain image;
generating a feature vector for the test H&E stain image based on the sampled subset of non-overlapping image tiles; and
predicting a hormone receptor status by applying a machine-learned prediction model to the feature vector for the test H&E stain image, wherein the machine-learned prediction model is trained using a first set of H&E stain images from a first set of tissue samples having a positive hormone receptor status and a second set of H&E stain images from a second set of tissue samples having a negative hormone receptor status.