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 …

Medical anomaly detection is a crucial yet challenging task aimed at recognizing abnormal
images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most …

Self-supervised anomaly detection and localization are critical to real-world scenarios in
which collecting anomalous samples and pixel-wise labeling is tedious or infeasible, even …

Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity,
texture, shape, size, and location. Comprehensive sets of data and annotations are difficult …

Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust
deployment of machine learning algorithms in medicine. When the algorithms encounter …

Supervised learning of every possible pathology is unrealistic for many primary care
applications like health screening. Image anomaly detection methods that learn normal …

Image classification models deployed in the real world may receive inputs outside the
intended data distribution. For critical applications such as clinical decision making, it is …

Anomaly detection (AD) is a fundamental research problem in machine learning and
computer vision with practical applications in industrial inspection video surveillance and …

Extensive research has been conducted on fault diagnosis of planetary gearboxes using
vibration signals and deep learning (DL) approaches. However, DL-based methods are …

Brain lesion segmentation provides a valuable tool for clinical diagnosis and research, and
convolutional neural networks (CNNs) have achieved unprecedented success in the …