  Coronary artery disease (CAD) remains the leading cause of death globally and
invasive coronary angiography (ICA) is considered the gold standard of
anatomical imaging evaluation when CAD is suspected. However, risk evaluation
based on ICA has several limitations, such as visual assessment of stenosis
severity, which has significant interobserver variability. This motivates to
development of a lesion classification system that can support specialists in
their clinical procedures. Although deep learning classification methods are
well-developed in other areas of medical imaging, ICA image classification is
still at an early stage. One of the most important reasons is the lack of
available and high-quality open-access datasets. In this paper, we reported a
new annotated ICA images dataset, CADICA, to provide the research community
with a comprehensive and rigorous dataset of coronary angiography consisting of
a set of acquired patient videos and associated disease-related metadata. This
dataset can be used by clinicians to train their skills in angiographic
assessment of CAD severity and by computer scientists to create computer-aided
diagnostic systems to help in such assessment. In addition, baseline
classification methods are proposed and analyzed, validating the functionality
of CADICA and giving the scientific community a starting point to improve CAD
detection.
