Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

Recent trends in semi-supervised learning have significantly boosted the performance of 3D
semi-supervised medical image segmentation. Compared with 2D images, 3D medical …

It is known that annotations for 3D medical image segmentation tasks are laborious, time-
consuming and expensive. Considering the similarities existing in inter-slice and inter …

Medical image segmentation typically necessitates a large and precisely annotated dataset.
However, obtaining pixel-wise annotation is a labor-intensive task that requires significant …

As deep learning methods continue to improve medical image segmentation performance,
data annotation is still a big bottleneck due to the labor-intensive and time-consuming …

Medical image segmentation and registration are very important related steps in clinical
medical diagnosis. In the past few years, deep learning techniques for joint segmentation …

Few-shot segmentation (FSS) models have gained popularity in medical imaging analysis
due to their ability to generalize well to unseen classes with only a small amount of …

Background Deep learning has presented great potential in accurate MR image
segmentation when enough labeled data are provided for network optimization. However …

Despite significant progress in 3D medical image segmentation using deep learning,
manual annotation remains a labor-intensive bottleneck. Self-supervised mask propagation …

Layer segmentation is important to quantitative analysis of retinal optical coherence
tomography (OCT). Recently, deep learning based methods have been developed to …