  Coronary Artery Diseases (CADs) although preventable, are one of the leading
causes of death and disability. Diagnosis of these diseases is often difficult
and resource intensive. Angiographic imaging segmentation of the arteries has
evolved as a tool of assistance that helps clinicians make an accurate
diagnosis. However, due to the limited amount of data and the difficulty in
curating a dataset, the task of segmentation has proven challenging. In this
study, we introduce the use of pseudo-labels to address the issue of limited
data in the angiographic dataset to enhance the performance of the baseline
YOLO model. Unlike existing data augmentation techniques that improve the model
constrained to a fixed dataset, we introduce the use of pseudo-labels generated
on a dataset of separate related task to diversify and improve model
performance. This method increases the baseline F1 score by 9% in the
validation data set and by 3% in the test data set.
