Cerebral small vessel disease (SVD) is common during ageing and can present as stroke,
cognitive decline, neurobehavioural symptoms, or functional impairment. SVD frequently …

Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

In many medical imaging and classical computer vision tasks, the Dice score and Jaccard
index are used to evaluate the segmentation performance. Despite the existence and great …

Deep generative models have emerged as promising tools for detecting arbitrary anomalies
in data, dispensing with the necessity for manual labelling. Recently, autoregressive …

Biomedical image segmentation is a crucial part of both scientific research and clinical care.
With enough labelled data, deep learning models can be trained to accurately automate …

U-Nets are a go-to neural architecture across numerous tasks for continuous signals on a
square such as images and Partial Differential Equations (PDE), however their design and …