Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

The lack of annotated medical images limits the performance of deep learning models,
which usually need large-scale labelled datasets. Few-shot learning techniques can reduce …

Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …

Deformable image registration (DIR), aiming to find spatial correspondence between
images, is one of the most critical problems in the domain of medical image analysis. In this …

The main objective of anatomically plausible results for deformable image registration is to
improve model's registration accuracy by minimizing the difference between a pair of fixed …

In medical image segmentation, supervised deep networks' success comes at the cost of
requiring abundant labeled data. While asking domain experts to annotate only one or a few …

Background Accurate evaluation of the axillary lymph node (ALN) status is needed for
determining the treatment protocol for breast cancer (BC). The value of magnetic resonance …

Understanding why deep nets can classify data in large dimensions remains a challenge. It
has been proposed that they do so by becoming stable to diffeomorphisms, yet existing …

Recently, deep learning-based brain segmentation methods have achieved great success.
However, most approaches focus on supervised segmentation, which requires many high …

In medical imaging, a major limitation of supervised Deep Neural Network is the need of
large annotated datasets. Current data augmentation methods, though quite efficient to …