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

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

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

This paper presents an overview of the second edition of the HEad and neCK TumOR
(HECKTOR) challenge, organized as a satellite event of the 24th International Conference …

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 …

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

Pathological brain appearances may be so heterogeneous as to be intelligible only as
anomalies, defined by their deviation from normality rather than any specific set of …

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 …

Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-
the-art results in semantic segmentation for numerous medical imaging applications …

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 …