Data Augmentation through Pseudolabels in Automatic Region Based
  Coronary Artery Segmentation for Disease Diagnosis
Sandesh Pokhrel Sanjay Bhandari Eduard Vazquez Yash Raj Shrestha Binod Bhattarai
Coronary Artery Diseases(CADs) though preventable are one of the leading
causes of death and disability. Diagnosis of these diseases is often difficult
and resource intensive. Segmentation of arteries in angiographic images has
evolved as a tool for assistance, helping clinicians in making 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 idea of using pseudolabels as a data augmentation
technique to improve the performance of the baseline Yolo model. This method
increases the F1 score of the baseline by 9% in the validation dataset and by
3% in the test dataset.