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 …

Existing B-mode ultrasound (BUS) based computer-aided diagnosis (CAD) for
developmental dysplasia of the hip (DDH) is mainly developed based on the Graf's method …