Generative adversarial network medical image generation for training of a classifier

A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to implement a machine learning training model that operates to:

• train an image generator of a generative adversarial network (GAN) to generate medical images approximating actual medical images;
• augment a set of training medical images to include one or more generated medical images generated by the image generator of the GAN;
• train a machine learning model based on the augmented set of training medical images to identify anomalies in medical images;
• and
• apply the trained machine learning model to new medical image inputs to classify the medical images as having an anomaly or not, wherein the machine learning model is a discriminator of the GAN which is configured to receive, as input, actual labeled medical image data, actual unlabeled medical image data, and generated medical image data generated by the image generator of the GAN, and wherein training the machine learning model comprises training the discriminator to generate an output comprising an output value for each of a plurality of classifications, and wherein the plurality of classifications comprises a first classification for real-normal image data indicating input image data to be actual image data representing a normal medical condition, at least one second classification for real-abnormal image data indicating input image data to be actual image data representing a corresponding abnormal medical condition, and a third classification for generated image data indicating input image data to be image data generated by the image generator.

An apparatus comprising:

• at least one processor;
• and
• at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to implement a machine learning training model that operates to:
• train an image generator of a generative adversarial network (GAN) to generate medical images approximating actual medical images;
• augment a set of training medical images to include one or more generated medical images generated by the image generator of the GAN;
• train a machine learning model based on the augmented set of training medical images to identify anomalies in medical images;
• and
• apply the trained machine learning model to new medical image inputs to classify the medical images as having an anomaly or not, wherein the machine learning model is a discriminator of the GAN which is configured to receive, as input, actual labeled medical image data, actual unlabeled medical image data, and generated medical image data generated by the image generator of the GAN, and wherein training the machine learning model comprises training the discriminator to generate an output comprising an output value for each of a plurality of classifications, and wherein the plurality of classifications comprises a first classification for real-normal image data indicating input image data to be actual image data representing a normal medical condition, at least one second classification for real-abnormal image data indicating input image data to be actual image data representing a corresponding abnormal medical condition, and a third classification for generated image data indicating input image data to be image data generated by the image generator.