Artifact Detection

Artifact detection in various data modalities, including images, videos, and physiological signals, aims to identify and remove or mitigate distortions that hinder accurate analysis and interpretation. Current research focuses on developing robust and efficient algorithms, often employing deep learning architectures like convolutional neural networks, vision transformers, and variational autoencoders, sometimes combined with ensemble methods or semi-supervised techniques like label propagation. These advancements are crucial for improving the reliability of automated analysis in diverse fields, ranging from medical image diagnostics and video quality assessment to brain-computer interfaces and physiological signal processing, ultimately leading to more accurate and efficient applications.