Knockout Autoencoder for Detecting Anomalies in Biomedical Images

A computer program product comprising a non-transitory computer readable medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to implement a knockout autoencoder engine for detecting anomalies in biomedical images, wherein the computer readable program causes the computing device to:

• train a neural network to be used as a knockout autoencoder that predicts an original based on an input image;
• provide, by the knockout autoencoder engine, a biomedical image as the input image to the neural network;
• output, by the neural network, a probability distribution for each pixel in the biomedical image, wherein each probability distribution represents a predicted probability distribution of expected pixel values for a given pixel in the biomedical image;
• determine, by an anomaly detection component executing within the knockout autoencoder engine, a probability that each pixel has an expected value based on the probability distributions to form a plurality of expected pixel probabilities;
• detect, by the anomaly detection component, an anomaly in the biomedical image based on the plurality of expected pixel probabilities;
• mark, by an anomaly marking component executing within the knockout autoencoder engine, the detected anomaly in the biomedical image to form a marked biomedical image;
• and
• output, by the knockout autoencoder engine, the marked biomedical image.

An apparatus, comprising:

• a processor;
• and
• a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to implement a knockout autoencoder engine for detecting anomalies in biomedical images, wherein the instructions cause the processor to:
• train a neural network to be used as a knockout autoencoder that predicts an original based on an input image;
• provide, by the knockout autoencoder engine, a biomedical image as the input image to the neural network;
• output, by the neural network, a probability distribution for each pixel in the biomedical image, wherein each probability distribution represents a predicted probability distribution of expected pixel values for a given pixel in the biomedical image;
• determine, by an anomaly detection component executing within the knockout autoencoder engine, a probability that each pixel has an expected value based on the probability distributions to form a plurality of expected pixel probabilities;
• detect, by the anomaly detection component, an anomaly in the biomedical image based on the plurality of expected pixel probabilities;
• mark, by an anomaly marking component executing within the knockout autoencoder engine, the detected anomaly in the biomedical image to form a marked biomedical image;
• and
• output, by the knockout autoencoder engine, the marked biomedical image.