Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …

Anomaly detection with weakly supervised video-level labels is typically formulated as a
multiple instance learning (MIL) problem, in which we aim to identify snippets containing …

Deep unsupervised approaches are gathering increased attention for applications such as
pathology detection and segmentation in medical images since they promise to alleviate the …

Despite most existing anomaly detection studies assume the availability of normal training
samples only, a few labeled anomaly examples are often available in many real-world …

Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two
challenges: 1) work effectively on both multi-class (eg, lesion classification) and multi-label …

This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one
single detection model that can generalize to detect anomalies in diverse datasets from …

Abstract State-of-the-art (SOTA) anomaly segmentation approaches on complex urban
driving scenes explore pixel-wise classification uncertainty learned from outlier exposure, or …

One-class classification (OCC) aims to learn an effective data description to enclose all
normal training samples and detect anomalies based on the deviation from the data …

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 …

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 …