US 11,798,662 B2
Methods for identifying biological material by microscopy
Alistair Cumming, Mornington (AU); Luan Duong Minh Lam, Hawthorn (AU); Christopher McCarthy, Hawthorn (AU); Michelle Dunn, Hawthorn (AU); Luke Gavin, Hawthorn (AU); Samar Kattan, Hawthorn (AU); and Antony Tang, Hawthorn (AU)
Assigned to VERDICT HOLDINGS PTY LTD, Mornington (AU)
Appl. No. 16/978,000
Filed by VERDICT HOLDINGS PTY LTD, Mornington (AU)
PCT Filed Mar. 5, 2019, PCT No. PCT/AU2019/050184
§ 371(c)(1), (2) Date Sep. 3, 2020,
PCT Pub. No. WO2019/169432, PCT Pub. Date Sep. 12, 2019.
Claims priority of application No. 2018900739 (AU), filed on Mar. 7, 2018.
Prior Publication US 2021/0248419 A1, Aug. 12, 2021
Int. Cl. G06T 7/00 (2017.01); G16H 10/40 (2018.01); G06T 7/194 (2017.01); G06T 7/11 (2017.01); G06T 7/73 (2017.01); G16H 30/40 (2018.01); G06F 16/51 (2019.01); G06V 20/69 (2022.01); G06F 18/214 (2023.01); G06F 18/40 (2023.01); G06V 10/94 (2022.01); G06N 20/00 (2019.01)
CPC G16H 10/40 (2018.01) [G06F 16/51 (2019.01); G06F 18/2148 (2023.01); G06F 18/40 (2023.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06T 7/73 (2017.01); G06V 10/945 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G16H 30/40 (2018.01); G06N 20/00 (2019.01); G06T 2207/10056 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20092 (2013.01); G06T 2207/20132 (2013.01); G06T 2207/30004 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A computer-implemented method for training a computer-implemented learning method to identify a genus or species of a parasite ovum in a sample, the method comprising:
accessing a plurality of computer-readable training images, the training images being obtained by light microscopy of one or more samples containing a parasite ovum and a non-parasite ovum material;
using a first subset of the plurality of computer-readable training images to perform human-supervised machine learning to distinguish images containing a parasite ovum from non-parasite ovum material to provide a machine learning model configured to distinguish parasite ovum material from non-parasite ovum material;
using a second subset of the plurality of computer-readable training images to perform frame cropping, the frame cropping comprising identifying a parasite ovum and cropping one or more of the plurality of computer-readable training images so as to produce one or more cropped computer-readable images, each of the one or more cropped computer-readable images showing predominantly the parasite ovum;
by human means identifying the genus or species of the parasite ovum in each of the one or more cropped computer-readable images where identification is possible;
associating an identification label with each of the one or more cropped computer-readable images where identification was possible;
applying a computer-implemented deep learning network feature extraction method to each labelled cropped computer-readable image; and
applying the machine learning model to each cropped computer-readable image to determine if the cropped computer-readable image contains a parasite ovum,
wherein the computer-implemented learning method is configured to associate one or more features extracted by the feature extraction method with a parasite ovum.