Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

Transformer, the model of choice for natural language processing, has drawn scant attention
from the medical imaging community. Given the ability to exploit long-term dependencies …

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 …

Foundation model, which is pre-trained on broad data and is able to adapt to a wide range
of tasks, is advancing healthcare. It promotes the development of healthcare artificial …

Recently, multi-modal vision-language foundation models have gained significant attention
in the medical field. While these models offer great opportunities, they still face crucial …

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

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 …

Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional
performance across numerous applications. However, Model Inversion (MI) attacks, which …

Electron microscopy (EM) images are notoriously challenging to segment due to their
complex structures and lack of effective annotations. Fortunately, large-scale self-supervised …

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