Abnormality Detection

Abnormality detection aims to identify unusual patterns or deviations from the norm within data, with applications spanning medical imaging, industrial quality control, and autonomous driving. Current research emphasizes the development of robust and generalizable models, often employing deep learning architectures like convolutional neural networks and transformers, to address challenges such as data scarcity, class imbalance, and the need for zero-shot or weakly supervised learning. These advancements hold significant promise for improving diagnostic accuracy in healthcare, enhancing efficiency in various industries, and enabling safer and more reliable autonomous systems.