US 12,001,935 B2
Computer-implemented method, computer program product and system for analysis of cell images
Rickard Sjögren, Umeå (SE); and Johan Trygg, Umeå (SE)
Assigned to SARTORIUS STEDIM DATA ANALYTICS AB, Umeå (SE)
Appl. No. 17/273,712
Filed by SARTORIUS STEDIM DATA ANALYTICS AB, Umeå (SE)
PCT Filed Sep. 5, 2019, PCT No. PCT/EP2019/073695
§ 371(c)(1), (2) Date Mar. 4, 2021,
PCT Pub. No. WO2020/049098, PCT Pub. Date Mar. 12, 2020.
Claims priority of application No. 18192649 (EP), filed on Sep. 5, 2018; and application No. 19180972 (EP), filed on Jun. 18, 2019.
Prior Publication US 2021/0350113 A1, Nov. 11, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 18/213 (2023.01); G06F 18/22 (2023.01); G06N 3/04 (2023.01); G06N 3/048 (2023.01); G06N 3/08 (2023.01); G06V 20/69 (2022.01)
CPC G06N 3/04 (2013.01) [G06F 18/213 (2023.01); G06F 18/22 (2023.01); G06N 3/048 (2023.01); G06N 3/08 (2013.01); G06V 20/698 (2022.01)] 14 Claims
OG exemplary drawing
 
1. A computer-implemented method for analysis of cell images, comprising:
obtaining a deep neural network (100) and at least a part of a training dataset used for training the deep neural network, the deep neural network comprising a plurality of hidden layers and being trained by using the training dataset, the training dataset including a plurality of possible cell images that can be input to the deep neural network;
obtaining first sets of intermediate output values that are output from at least one of the plurality of hidden layers, each of the first sets of intermediate output values obtained by inputting a different one of the possible cell images included in said at least a part of the training dataset;
constructing/fitting a latent variable model using the first sets of intermediate output values, the latent variable model providing a mapping of the first sets of intermediate output values to first sets of projected values in a sub-space that has a dimension lower than a dimension of the sets of the intermediate outputs;
receiving a new cell image to be input to the deep neural network; and
storing the latent variable model and the first sets of projected values in a storage medium.