Abstract Vision Transformers (ViT) s have shown great performance in self-supervised
learning of global and local representations that can be transferred to downstream …

Benefiting from the large-scale archiving of digitized whole-slide images (WSIs), computer-
aided diagnosis has been well developed to assist pathologists in decision-making. Content …

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 …

In the field of medical image analysis, supervised deep learning strategies have achieved
significant development, while these methods rely on large labeled datasets. Self …

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

Fully-supervised deep learning segmentation models are inflexible when encountering new
unseen semantic classes and their fine-tuning often requires significant amounts of …

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

The Segment Anything Model (SAM) has recently emerged as a groundbreaking model in
the field of image segmentation. Nevertheless, both the original SAM and its medical …

Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training,
due to their sharing of numerous same semantic regions. However, the lack of the semantic …

Self-supervised learning (SSL) has recently achieved promising performance for 3D medical
image analysis tasks. Most current methods follow existing SSL paradigm originally …