Advancing medical image segmentation via pseudo-labeling of public datasets

Abstract

Our study explores the difficulties and possible resolutions in the domain of medical image segmentation, with a special emphasis on utilizing unlabeled public datasets to improve tumor segmentation. We suggest a strategy that incorporates pseudolabeling methodologies with real-world data to enhance the learning potential of segmentation models. Yet, the findings imply that while improvements in model performance exist, they are not substantial. The research underscores the paramount importance of data quality over quantity, emphasizing that image characteristics influence the effectiveness of the process more than the total number of images.