US 12,248,533 B2
Efficient training and accuracy improvement of imaging based assay
Xing Li, Metuchen, NJ (US); Wu Chou, Basking Ridge, NJ (US); Stephen Y. Chou, Princeton, NJ (US); Wei Ding, East Windsor, NJ (US); and Ji Qi, Hillsborough, NJ (US)
Assigned to Essenlix Corporation, Monmouth Junction, NJ (US)
Filed by Essenlix Corporation, Monmouth Junction, NJ (US)
Filed on Jan. 24, 2023, as Appl. No. 18/101,109.
Application 18/101,109 is a continuation of application No. 17/431,345, granted, now 11,593,590, previously published as PCT/US2020/062445, filed on Nov. 25, 2020.
Claims priority of provisional application 62/940,242, filed on Nov. 25, 2019.
Prior Publication US 2023/0169151 A1, Jun. 1, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 18/214 (2023.01); G06N 3/042 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06T 5/50 (2006.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/70 (2017.01); G06V 10/22 (2022.01)
CPC G06F 18/2148 (2023.01) [G06N 3/042 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 5/50 (2013.01); G06T 7/0002 (2013.01); G06T 7/11 (2017.01); G06T 7/70 (2017.01); G06V 10/22 (2022.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/30204 (2013.01)] 41 Claims
OG exemplary drawing
 
1. A method of training a machine learning model for an image based assay, wherein a sample in the assay is, during a test, imaged by an imaging system with an imperfection, and wherein the sample contains or is suspected of containing an analyte; comprising:
having the sample forming a thin layer on an imaging area of a sample holder, wherein the sample holder comprises one or more monitoring marks in the imaging area, and wherein at least one of the geometric or optical properties of the one or more monitoring marks is predetermined and known;
imaging, using the imaging system, an original image of the sample on the imaging area of the sample holder;
correcting an imperfection in the original image using the at least one of the geometric or optical properties of the one or more monitoring marks, to generate a corrected image; and
training a machine learning model using the corrected image to generate a trained model for measuring the analyte,
wherein the sample holder comprises:
a first plate and a second plate that are movable to each other in different configurations, including an open configuration and a closed configuration, and the sample is disposed between the first and second plates;
a plurality of spacers on one of the two plates or both, wherein the plurality of spacers are situated between the first and second plates in the closed configuration,
wherein, in the open configuration, the two plates are separated apart, and the spacing between the first and second plates is not regulated by the spacers to facilitate the deposition of the sample on one or both of the first and second plates; and
in the closed configuration that is configured after the sample deposition in the open configuration, at least part of the sample is compressed by the first and second plates into a layer of substantially uniform thickness, and the substantially uniform thickness of the layer is regulated by the two plates and the spacers.