Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

In semi-supervised medical image segmentation, there exist empirical mismatch problems
between labeled and unlabeled data distribution. The knowledge learned from the labeled …

Abstract Despite that Convolutional Neural Networks (CNNs) have achieved promising
performance in many medical image segmentation tasks, they rely on a large set of labeled …

Recently, deep learning with Convolutional Neural Networks (CNNs) and Transformers has
shown encouraging results in fully supervised medical image segmentation. However, it is …

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively
exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ …

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

Whole abdominal organ segmentation is important in diagnosing abdomen lesions,
radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from …

For medical image segmentation, contrastive learning is the dominant practice to improve
the quality of visual representations by contrasting semantically similar and dissimilar pairs …

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 …

Semi-supervised learning (SSL) has recently demonstrated great success in medical image
segmentation, significantly enhancing data efficiency with limited annotations. However …