Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …

The contrastive pre-training of a recognition model on a large dataset of unlabeled data
often boosts the model's performance on downstream tasks like image classification …

Semi-supervised learning (SSL), which aims at leveraging a few labeled images and a large
number of unlabeled images for network training, is beneficial for relieving the burden of …

Semi-supervised segmentation remains challenging in medical imaging since the amount of
annotated medical data is often scarce and there are many blurred pixels near the adhesive …

Although recent works in semi-supervised learning (SemiSL) have accomplished significant
success in natural image segmentation, the task of learning discriminative representations …

Semi-supervised learning has garnered significant interest as a method to alleviate the
burden of data annotation. Recently, semi-supervised medical image segmentation has …

Deep learning-based semi-supervised learning (SSL) algorithms are promising in reducing
the cost of manual annotation of clinicians by using unlabelled data, when developing …

The success of deep learning methods in medical image segmentation tasks usually
requires a large amount of labeled data. However, obtaining reliable annotations is …

Deep learning-based semi-supervised learning (SSL) algorithms have led to promising
results in medical images segmentation and can alleviate doctors' expensive annotations by …

Consistency regularization and pseudo labeling-based semi-supervised methods perform
co-training using the pseudo labels from multi-view inputs. However, such co-training …