The remarkable success of deep learning in various domains relies on the availability of
large-scale annotated datasets. However, obtaining annotations is expensive and requires …

Deep learning has recently demonstrated considerable promise for a variety of computer
vision tasks. However, in many practical applications, large-scale labeled datasets are not …

The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO)
in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 …

The performance of deep neural networks improves with more annotated data. The problem
is that the budget for annotation is limited. One solution to this is active learning, where a …

Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …

Active learning aims to develop label-efficient algorithms by sampling the most
representative queries to be labeled by an oracle. We describe a pool-based semi …

The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …