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

Purpose Building accurate and robust artificial intelligence systems for medical image
assessment requires the creation of large sets of annotated training examples. However�…

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�…

A key requirement for the success of supervised deep learning is a large labeled dataset-a
condition that is difficult to meet in medical image analysis. Self-supervised learning (SSL)�…

The scarcity of high-quality annotated medical imaging datasets is a major problem that
collides with machine learning applications in the field of medical imaging analysis and�…

In the field of medical image analysis, supervised deep learning strategies have achieved
significant development, while these methods rely on large labeled datasets. Self�…

Self-supervised learning (SSL) has achieved remarkable performance in various medical
imaging tasks by dint of priors from massive unlabeled data. However, regarding a specific�…

Medical image segmentation has seen significant progress through the use of supervised
deep learning. Hereby, large annotated datasets were employed to reliably segment�…

Semi-supervised learning (SSL), which aims at leveraging a few labeled images and a large
number of unlabeled images for network training, is beneficial for relieving the burden of�…

Supervised machine learning provides state-of-the-art solutions to a wide range of computer
vision problems. However, the need for copious labelled training data limits the capabilities�…