Abstract Self-Supervised Learning (SSL) has demonstrated promising results in 3D medical
image analysis. However the lack of high-level semantics in pre-training still heavily hinders�…

The long-tailed distribution problem in medical image analysis reflects a high prevalence of
common conditions and a low prevalence of rare ones which poses a significant challenge�…

Human anatomy is the foundation of medical imaging and boasts one striking characteristic:
its hierarchy in nature, exhibiting two intrinsic properties:(1) locality: each anatomical�…

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�…

Expert annotation of 3D medical image for downstream analysis is resource-intensive,
posing challenges in clinical applications. Visual self-supervised learning (vSSL), though�…

Self-supervised learning (SSL) is an efficient pre-training method for medical image
analysis. However current research is mostly confined to certain modalities consuming�…

Humans effortlessly interpret images by parsing them into part-whole hierarchies; deep
learning excels in learning multi-level feature spaces but they often lack explicit coding of�…

The Vision Transformer (ViT) has demonstrated remarkable performance in Self-Supervised
Learning (SSL) for 3D medical image analysis. Mask AutoEncoder (MAE) for feature pre�…

Parameter-efficient fine-tuning (PEFT) that was initially developed for exploiting pre-trained
large language models has recently emerged as an effective approach to perform transfer�…

Automated segmentation is a fundamental medical image analysis task, which enjoys
significant advances due to the advent of deep learning. While foundation models have�…