Abstract Recent advances in Deep Learning have largely benefited from larger and more
diverse training sets. However, collecting large datasets for medical imaging is still a …

In recent years, significant progress has been achieved using deep neural networks (DNNs)
in obtaining human-level performance on various long-standing tasks. With the increased …

Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …

Semi-supervised learning has garnered significant interest as a method to alleviate the
burden of data annotation. Recently, semi-supervised medical image segmentation has …

Deep learning approaches have demonstrated great achievements in the field of computer-
aided medical image analysis, improving the precision of diagnosis across a range of …

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 techniques have achieved superior performance in computer-aided medical
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …

Brain lesion segmentation provides a valuable tool for clinical diagnosis and research, and
convolutional neural networks (CNNs) have achieved unprecedented success in the …

Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues,
particularly in the diagnosis and management of prostate cancer. With the widespread …

Most state-of-the-art techniques for medical image segmentation rely on deep-learning
models. These models, however, are often trained on narrowly-defined tasks in a supervised …