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�…

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

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

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�…

Deep learning highly relies on the quantity of annotated data. However, the annotations for
3D volumetric medical data require experienced physicians to spend hours or even days for�…

Due to the development of deep learning, an increasing number of research works have
been proposed to establish automated analysis systems for 3D volumetric medical data to�…

Collecting large-scale medical datasets with fully annotated samples for training of deep
networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in�…

The difficulties in both data acquisition and annotation substantially restrict the sample sizes
of training datasets for 3D medical imaging applications. Therefore, it is non-trivial to build�…

While making a tremendous impact in various fields, deep neural networks usually require
large amounts of labeled data for training which are expensive to collect in many�…

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