Transfer learning from natural image to medical image has established as one of the most
practical paradigms in deep learning for medical image analysis. However, to fit this�…

Transfer learning from natural images to medical images has been established as one of the
most practical paradigms in deep learning for medical image analysis. To fit this paradigm�…

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

The performance on deep learning is significantly affected by volume of training data.
Models pre-trained from massive dataset such as ImageNet become a powerful weapon for�…

Transfer learning has gained attention in medical image analysis due to limited annotated
3D medical datasets for training data-driven deep learning models in the real world. Existing�…

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�…

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

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�…

We investigate the effectiveness of a simple solution to the common problem of deep
learning in medical image analysis with limited quantities of labeled training data. The�…

Witnessed the development of deep learning, increasing number of studies try to build
computer aided diagnosis systems for 3D volumetric medical data. However, as the�…