Medical Anomaly Detection

Medical anomaly detection aims to identify abnormalities in medical images using machine learning, primarily focusing on unsupervised or few-shot learning scenarios due to limited labeled data. Current research heavily utilizes autoencoders, generative adversarial networks (GANs), and diffusion models, often incorporating techniques like contrastive learning, attention mechanisms, and image quality assessment to improve accuracy and localization of anomalies. These advancements hold significant promise for improving diagnostic accuracy, accelerating disease detection, and ultimately enhancing patient care by enabling efficient analysis of large medical image datasets.