Deep unsupervised representation learning has recently led to new approaches in the field
of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these …

The increasing digitization of medical imaging enables machine learning based
improvements in detecting, visualizing and segmenting lesions, easing the workload for …

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 …

Background Unsupervised learning can discover various unseen abnormalities, relying on
large-scale unannotated medical images of healthy subjects. Towards this, unsupervised …

We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies
(NSA), for training an end-to-end model for anomaly detection and localization using only …

Scarcity of annotated images hampers the building of automated solution for reliable COVID-
19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein …

As the labeled anomalous medical images are usually difficult to acquire, especially for rare
diseases, the deep learning based methods, which heavily rely on the large amount of …

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 …

Anomaly detection in the automated optical quality inspection is of great important for
guaranteeing the surface quality of industrial products. Most related methods are based on …