Generalizing the medical image segmentation algorithms to unseen domains is an important
research topic for computer-aided diagnosis and surgery. Most existing methods require a …

Generalising deep models to new data from new centres (termed here domains) remains a
challenge. This is largely attributed to shifts in data statistics (domain shifts) between source …

Deep convolutional neural networks have been highly effective in segmentation tasks.
However, segmentation becomes more difficult when training images include many complex …

Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power
in extracting lesion-related features. Building such large and well-designed medical …

Segment anything model (SAM) has emerged as the leading approach for zero-shot
learning in segmentation tasks, offering the advantage of avoiding pixel-wise annotations. It …

Deep convolutional neural networks have been highly effective in segmentation tasks.
However, high performance often requires large datasets with high-quality annotations …

Network ensemble aims to obtain better results by aggregating the predictions of multiple
weak networks, in which how to keep the diversity of different networks plays a critical role in …

The rapid evolution of deep learning has significantly advanced the field of medical image
analysis. However, despite these achievements, the further enhancement of deep learning …

Noisy labels collected with limited annotation cost prevent medical image segmentation
algorithms from learning precise semantic correlations. Previous segmentation arts of …

Semi-supervised learning has emerged as a widely adopted technique in the field of
medical image segmentation. The existing works either focuses on the construction of …