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 …

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 …

Recent work has shown that label-efficient few-shot learning through self-supervision can
achieve promising medical image segmentation results. However, few-shot segmentation …

Self-supervised pretraining has been observed to be effective at improving feature
representations for transfer learning, leveraging large amounts of unlabelled data. This …

With the recent development of deep learning, the classification and segmentation tasks of
computer-aided diagnosis (CAD) using non-contrast head computed tomography (NCCT) …

Self-supervised pre-training, based on the pretext task of instance discrimination, has fuelled
the recent advance in label-efficient object detection. However, existing studies focus on pre …

Due to the high human cost of annotation, it is non-trivial to curate a large-scale medical
dataset that is fully labeled for all classes of interest. Instead, it would be convenient to …

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

Deep learning-based approaches for content-based image retrieval (CBIR) of computed
tomography (CT) liver images is an active field of research, but suffer from some critical …

Fairness-aware learning mainly focuses on single task learning (STL). The fairness
implications of multi-task learning (MTL) have only recently been considered and a seminal …