Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation
This work addresses data scarcity for clinicians in diagnosing coronary artery diseases, but it is incremental as it builds on existing pseudo-label and data augmentation techniques.
The study tackled the challenge of limited data for coronary artery segmentation in angiographic images by using pseudo-labels generated from a related task to augment the dataset, resulting in a 9% increase in F1 score on validation data and a 3% increase on test data compared to a baseline YOLO model.
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.