Image data validation for medical systems

Abstract

Data validation is the process of ensuring that the input to a data processing pipeline is correct and useful. It is a critical part of software systems running in production. Image processing systems are no different, whereby problems with data acquisition, file corruption or data transmission, may lead to a wide range of unexpected issues in the acquired images. Until now, most image processing systems of this type involved a human in the loop that could detect these errors before further processing. With the advent of powerful deep learning methods, tools for medical image processing are becoming increasingly autonomous and can go from data acquisition to final medical diagnosis without any human interaction. However, deep networks are known for their inability to detect corruption or errors in the input data. To overcome this, we present a validation method that learns the appearance of images in the training dataset that was used to train the deep network, and is able to identify when an input image deviates from the training distribution and therefore cannot be safely analyzed. We experimentally assess the validity of our method and compare it with different baselines, reaching an improvement of more than 10% points on all considered datasets.