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Coronary Artery Diseases (CADs) although preventable, are one of the leadingcauses of death and disability. Diagnosis of these diseases is often difficultand resource intensive. Angiographic imaging segmentation of the arteries hasevolved as a tool of assistance that helps clinicians make an accuratediagnosis. However, due to the limited amount of data and the difficulty incurating a dataset, the task of segmentation has proven challenging. In thisstudy, we introduce the use of pseudo-labels to address the issue of limiteddata in the angiographic dataset to enhance the performance of the baselineYOLO model. Unlike existing data augmentation techniques that improve the modelconstrained to a fixed dataset, we introduce the use of pseudo-labels generatedon a dataset of separate related task to diversify and improve modelperformance. This method increases the baseline F1 score by 9% in thevalidation data set and by 3% in the test data set.

Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation