Self-supervised learning methods have witnessed a recent surge of interest after proving
successful in multiple application fields. In this work, we leverage these techniques, and we�…

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

Self-supervised learning approaches leverage unlabeled samples to acquire generic
knowledge about different concepts, hence allowing for annotation-efficient downstream�…

Self-supervised learning (SSL) opens up huge opportunities for medical image analysis that
is well known for its lack of annotations. However, aggregating massive (unlabeled) 3D�…

Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the�…

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�…

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�…

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�…

Recent advances in self-supervised learning (SSL) in computer vision are primarily
comparative, whose goal is to preserve invariant and discriminative semantics in latent�…

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�…