Tesfaye Eristu, Abera Mekis, Ritbano Ahmed Abdo
Determinants of postpartum long-acting reversible contraceptives in the extended postpartum period in Shashago district, Central Ethiopia: a cross-sectional study conducted in the community
Contraception and reproductive medicine
deep learning algorithms demonstrated impressive performance leveraging large labeled data however acquiring pixellevel annotations medical image analysis especially segmentation tasks costly timeconsuming posing challenges supervised learning techniques existing semisupervised methods tend underutilize representations unlabeled data handle labeled unlabeled data separately neglecting interdependencies address issue introduce dataaugmented attentiondecoupled contrastive model dadc model incorporates attention decoupling module utilizes contrastive learning effectively distinguish foreground background significantly improving segmentation accuracy approach integrates augmentation technique merges information labeled unlabeled data notably boosting network performance especially scenarios limited labeled data conducted comprehensive experiments abus dataset results demonstrate dadc outperforms existing segmentation methods terms segmentation performance