Anomaly detection is a crucial task across different domains and data types. However,
existing anomaly detection models are often designed for specific domains and modalities …

While there has been a growing research interest in developing out-of-distribution (OOD)
detection methods, there has been comparably little discussion around how these methods …

High-performing out-of-distribution (OOD) detection both anomaly and novel class is an
important prerequisite for the practical use of classification models. In this paper we focus on …

An assumption-free, disease-agnostic pathology detector and segmentor could often be
seen as one of the holy grails of medical image analysis. Building on this promise, un …

Deep Neural Networks (DNNs) have achieved astonishing results in the last two decades,
fueled by ever larger datasets and the availability of high performance compute hardware …