Pathological brain lesions exhibit diverse appearance in brain images, making it difficult to
train supervised detection solutions due to the lack of comprehensive data and annotations …

Reducing the requirement for densely annotated masks in medical image segmentation is
important due to cost constraints. In this paper, we consider the problem of inferring pixel …

Abstract Machine learning has received increasing attention in computer science in recent
years and many types of methods have been proposed. In computer networks, little attention …

Deep Neural Networks are actively being used in the design of autonomous Cyber-Physical
Systems (CPSs). The advantage of these models is their ability to handle high-dimensional …

Cardiovascular diseases (CVDs) remain a leading cause of death globally. According to the
American Heart Association, approximately 19.1 million deaths were attributed to CVDs in …

In this study we evaluate 33 unsupervised anomaly detection algorithms on 52 real-world
multivariate tabular data sets, performing the largest comparison of unsupervised anomaly …

The class imbalance problem occurs when there is an unequal distribution of classes in a
dataset and is a significant issue in various artificial intelligence applications. This study …

Variational autoencoder (VAE) is a deep generative model for unsupervised learning,
allowing to encode observations into the meaningful latent space. VAE is prone to …

Anomaly detection and localization in images is a growing field in computer vision. In this
area a seemingly understudied problem is anomaly clustering ie identifying and grouping …

Medical images contain various abnormal regions, most of which are closely related to the
lesions or diseases. The abnormality or lesion is one of the major concerns during clinical …