Double-mix pseudo-label framework: enhancing semi-supervised segmentation on category-imbalanced CT volumes.
Journal:
International journal of computer assisted radiology and surgery
PMID:
39932621
Abstract
PURPOSE: Deep-learning-based supervised CT segmentation relies on fully and densely labeled data, the labeling process of which is time-consuming. In this study, our proposed method aims to improve segmentation performance on CT volumes with limited annotated data by considering category-wise difficulties and distribution.