Deep learning has recently demonstrated considerable promise for a variety of computer
vision tasks. However, in many practical applications, large-scale labeled datasets are not …

Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …

Both limited annotation and domain shift are prevalent challenges in medical image
segmentation. Traditional semi-supervised segmentation and unsupervised domain …

Recently, significant progress has been made in consistency regularization-based semi-
supervised medical image segmentation. Typically, a consistency loss is applied to enforce …

Annotation scarcity has become a major obstacle for training powerful deep-learning models
for medical image segmentation, restricting their deployment in clinical scenarios. To …

Medical image segmentation models adapting to new tasks in a training-free manner
through in-context learning is an exciting advancement. Universal segmentation models aim …

Cardiac MRI segmentation is a significant research area in medical image processing,
holding immense clinical and scientific importance in assisting the diagnosis and treatment …

Semi-supervised medical image segmentation presents a compelling approach to
streamline large-scale image analysis, alleviating annotation burdens while maintaining …

Medical image diagnosis has developed rapidly under the impetus of the deep network.
Previous works mainly focus on improving the diagnostic accuracy of models, ie, first use a …

Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical
image segmentation in a fully supervised manner, but requires precise annotations. To …