Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …

Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Unsupervised anomaly detection with localization has many practical applications when
labeling is infeasible and, moreover, when anomaly examples are completely missing in the …

Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain
disease and monitor treatment as non-invasive imaging technology. MRI produces three …

Unsupervised representation learning has proved to be a critical component of anomaly
detection/localization in images. The challenges to learn such a representation are two-fold …

The detection of anomalous structures in natural image data is of utmost importance for
numerous tasks in the field of computer vision. The development of methods for …

Anomaly detection is a challenging task and usually formulated as an one-class learning
problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful …

Visual anomaly detection, an important problem in computer vision, is usually formulated as
a one-class classification and segmentation task. The student-teacher (ST) framework has …

We introduce a powerful student-teacher framework for the challenging problem of
unsupervised anomaly detection and pixel-precise anomaly segmentation in high-resolution …

The detection of anomalous structures in natural image data is of utmost importance for
numerous tasks in the field of computer vision. The development of methods for …