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

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

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

Transformers have made remarkable progress towards modeling long-range dependencies
within the medical image analysis domain. However, current transformer-based models …

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+ …

Medical report generation, which aims to automatically generate a long and coherent report
of a given medical image, has been receiving growing research interests. Existing …

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

Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …

Over the last decade, supervised deep learning on manually annotated big data has been
progressing significantly on computer vision tasks. But, the application of deep learning in …

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 …