Computer-implemented method, data processing apparatus, and computer program for image analysis

A computer implemented method of image analysis, the method comprising:

• receiving an input image;
• processing the input image using a trained machine-learning model to obtain analysis data of the input image, wherein the trained machine-learning model comprises:
• a trained encoder, iteratively trained using an input training dataset comprising real training data corresponding to a real modality and augmented training data, the iterative training having comprised:
• training a discriminator model by minimising a discriminator loss function using the input training dataset to obtain a trained discriminator, the trained discriminator being configured to discriminate whether input data is real data or augmented data;
• and
• training the encoder by maximising a discriminator error using the training dataset and the trained discriminator to obtain the trained encoder, the trained encoder being configured to invariantly encode real data and augmented data to a real representation space, such that representations preserve information about the real modality;
• and
• trained further downstream layers, trained by a multi-objective optimization procedure using the training dataset and the trained encoder.

A data processing apparatus comprising:

• a memory storing computer-executable instructions;
• and
• a processor configured to execute the instructions to:
• receive an input training dataset comprising real training data corresponding to a real modality and augmented training data;
• iteratively train an encoder of the machine-learning model to obtain a trained encoder, the iterative training comprising:
• training a discriminator model by minimising a discriminator loss function using the input training dataset to obtain a trained discriminator, the trained discriminator being configured to discriminate whether input data is real data or augmented data;
• and
• training the encoder by maximising a discriminator error using the training dataset and the trained discriminator to obtain a trained encoder, the trained encoder being configured to invariantly encode real data and augmented data to a real representation space, such that representations preserve information about the real modality.

A computer program comprising instructions which, when the program is executed by a computer, cause the computer to:

• receive an input training dataset comprising real training data corresponding to a real modality and augmented training data;
• iteratively train an encoder of the machine-learning model to obtain a trained encoder, the iterative training comprising:
• training a discriminator model by minimising a discriminator loss function using the input training dataset to obtain a trained discriminator, the trained discriminator being configured to discriminate whether input data is real data or augmented data;
• and
• training the encoder by maximising a discriminator error using the training dataset and the trained discriminator to obtain a trained encoder, the trained encoder being configured to invariantly encode real data and augmented data to a real representation space, such that representations preserve information about the real modality.

A non-transitory computer-readable storage medium comprising instructions which, when executed by a computer, cause the computer to:

• receive an input training dataset comprising real training data corresponding to a real modality and augmented training data;
• iteratively train an encoder of the machine-learning model to obtain a trained encoder, the iterative training comprising:
• training a discriminator model by minimising a discriminator loss function using the input training dataset to obtain a trained discriminator, the trained discriminator being configured to discriminate whether input data is real data or augmented data;
• and
• training the encoder by maximising a discriminator error using the training dataset and the trained discriminator to obtain a trained encoder, the trained encoder being configured to invariantly encode real data and augmented data to a real representation space, such that representations preserve information about the real modality.