Бібліографічний опис:
Mishchenko Roman. Advancing medical image segmentation via pseudo-labeling of public datasets. Ukrainian Catholic University, Faculty of Applied Sciences, Department of Computer Sciences. Lviv 2023, 36 p.
Короткий опис (реферат):
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.
