US 12,236,586 B2
System and method for classifying dermatological images using machine learning
Trevor Champagne, North York (CA)
Assigned to 2692873 Ontario Inc., Kamloops (CA)
Filed by 2692873 Ontario Inc., Toronto (CA)
Filed on Jan. 28, 2022, as Appl. No. 17/587,688.
Claims priority of provisional application 63/144,233, filed on Feb. 1, 2021.
Prior Publication US 2022/0245800 A1, Aug. 4, 2022
Int. Cl. G06T 7/00 (2017.01); G06F 3/01 (2006.01); G06N 20/00 (2019.01); G06T 3/40 (2006.01); G06T 7/11 (2017.01); G16H 30/20 (2018.01)
CPC G06T 7/0012 (2013.01) [G06F 3/013 (2013.01); G06N 20/00 (2019.01); G06T 3/40 (2013.01); G06T 7/11 (2017.01); G16H 30/20 (2018.01); G06T 2207/10016 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30088 (2013.01); G06T 2207/30168 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for training a machine learning model, the method comprising:
receiving a dataset comprising a plurality of medical images;
receiving, from a first single source, a respective label for each one of the plurality of medical images, the respective label being a positive response versus a negative response, wherein the positive response as the respective label for the respective medical image is an indication that the medical image has a quality sufficient for medical diagnosis, without indicating the medical diagnosis on the medical image;
receiving a gaze profile for each one of the plurality of medical images from the first single source;
dividing each one of the plurality of medical images into a plurality of medical image segments;
associating each one of the plurality of medical image segments with an image segment label based on the respective label for the respective medical image being divided;
for each medical image of the plurality of medical image segments from the plurality of medical images, when the respective image segment label is a positive response: generating a gaze label based on the number of gaze hits for each one of the plurality of medical image segments for the medical image based on the received gaze profile for the medical image, and associating each one of the plurality of medical image segments with the respective gaze label; and
sending a first set of labelled datasets to the machine learning model to train the machine learning model to identify image segments as having the quality sufficient for medical diagnosis for the first single source, wherein each labelled dataset in the first set includes a respective medical image segment of a medical image associated with a gaze label and an image segment label, the gaze label indicates that the number of gaze hit of the medical image segment being above a threshold, and the image segment label is the positive response.