Computer-implemented method, computer program product and system for analysis of cell images

A computer-implemented method comprising:

• receiving a new cell image to be input to a deep neural network (100), the deep neural network having a plurality of hidden layers and being trained using a training dataset that includes possible cell images that can be input to the deep neural network;
• obtaining a second set of intermediate output values that are output from at least one of the plurality of hidden layers of the deep neural network by inputting the received new cell image to the deep neural network;
• mapping, using a latent variable model stored in a storage medium, the second set of intermediate output values to a second set of projected values;
• and
• determining whether or not the received new cell image is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values,
• wherein the latent variable model stored in the storage medium is constructed by:
• obtaining first sets of intermediate output values that are output from said one of the plurality of hidden layers of the deep neural network, 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;
• and
• constructing the 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 of the latent variable model that has a dimension lower than a dimension of the sets of the intermediate outputs.

A computer program product comprising computer-readable instructions embodied on one or more non-transitory computer-readable media that, when loaded and run on a computer, cause the computer to perform a method comprising:

• receiving a new cell image to be input to a deep neural network, the deep neural network having a plurality of hidden layers and being trained using a training dataset that includes possible cell images that can be input to the deep neural network;
• obtaining a second set of intermediate output values that are output from at least one of the plurality of hidden layers of the deep neural network by inputting the received new cell image to the deep neural network;
• mapping, using a latent variable model stored in a storage medium, the second set of intermediate output values to a second set of projected values;
• and
• determining whether or not the received new cell image is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values,
• wherein the latent variable model stored in the storage medium is constructed by:
• obtaining first sets of intermediate output values that are output from said one of the plurality of hidden layers of the deep neural network, 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;
• and
• constructing the 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 of the latent variable model that has a dimension lower than a dimension of the sets of the intermediate outputs.

A system for data analysis, the system comprising:

• a storage medium (12) storing a training dataset used for training a deep neural network (100), the deep neural network comprising a plurality of hidden layers and being trained using the training dataset, the training dataset including possible cell images that can be input to the deep neural network;
• and
• a processor configured to perform a method for analysis of cell images, comprising:
• obtaining the deep neural network and at least a part of the training dataset used for training 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.