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

Recent studies on contrastive learning have achieved remarkable performance solely by
leveraging few labels in medical image segmentation. Existing methods mainly focus on …

Medical data often exhibits long-tail distributions with heavy class imbalance, which
naturally leads to difficulty in classifying the minority classes (ie, boundary regions or rare …

Since annotating medical images for segmentation tasks commonly incurs expensive costs,
it is highly desirable to design an annotation-efficient method to alleviate the annotation …

Homologous anatomical landmarks between medical scans are instrumental in quantitative
assessment of image registration quality in various clinical applications, such as MRI …