AutoQC#
An automated quality control pipeline for chest radiographs.
Version: 0.7.1
Designed for use prior to training a machine learning model or performing inference, AutoQC aims to:
Expedite quality control on chest radiograph datasets, giving developers rapid insights into their data;
Improve downstream model generalisation; and
Reduce the influence of confounding factors and contribute towards ethical AI.
In pursuit of these goals, AutoQC will:
Perform initial preprocessing;
Detect images that may not be suitable for training or inference; and
Detect and label images for possible confounding factors, e.g. radiographic projection or the presence of a pacemaker.
A pre-print of our paper is available on request:
Automated Quality Control of Chest Radiographs: Tools to Combat Shortcut Learning in COVID-19 Deep Learning Models. (submitted)
Selby IA., González Solares E., Breger A., et al. on behalf of the AIX-COVNET Collaboration.