US 12,217,483 B2
Systems and methods for processing electronic images for generalized disease detection
Belma Dogdas, Ridgewood, NJ (US); Christopher Kanan, Pittsford, NY (US); Thomas Fuchs, New York, NY (US); and Leo Grady, Darien, CT (US)
Assigned to PAIGE.AI, Inc., New York, NY (US)
Filed by PAIGE.AI, Inc., New York, NY (US)
Filed on Oct. 17, 2023, as Appl. No. 18/488,364.
Application 18/488,364 is a continuation of application No. 17/710,613, filed on Mar. 31, 2022, granted, now 11,823,436.
Application 17/710,613 is a continuation of application No. 17/380,595, filed on Jul. 20, 2021, granted, now 11,322,246, issued on May 3, 2022.
Application 17/380,595 is a continuation of application No. 17/126,865, filed on Dec. 18, 2020, granted, now 11,107,573, issued on Aug. 31, 2021.
Claims priority of provisional application 62/956,876, filed on Jan. 3, 2020.
Prior Publication US 2024/0046615 A1, Feb. 8, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 10/764 (2022.01); G06T 7/00 (2017.01); G06V 10/82 (2022.01); G06V 20/69 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01)
CPC G06V 10/764 (2022.01) [G06T 7/0012 (2013.01); G06V 10/82 (2022.01); G06V 20/698 (2022.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30096 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for processing electronic images, the method comprising:
receiving a generalized machine learning model;
receiving a plurality of training images, a quantity of training images being insufficient to generate a machine learning model that meets a threshold;
receiving a plurality of target specialized attributes each related to a respective training image of the plurality of training images;
generating a specialized machine learning model by modifying the generalized machine learning model based on the plurality of training images and respective target specialized attributes, the specialized machine learning model meeting the threshold, the specialized machine learning model being generated in accordance with a large-margin scheme built over one or more features of the generalized machine learning model;
receiving a target image corresponding to a target specimen;
applying the specialized machine learning model to the target image to determine at least one characteristic of the target image; and
outputting the at least one characteristic of the target image.