Artifact Disentanglement Network

Artifact disentanglement networks aim to separate unwanted artifacts from valuable data within various image and signal modalities, improving the accuracy and reliability of downstream analyses. Current research focuses on developing and applying these networks across diverse fields, including medical imaging (e.g., EEG, CT, MRI, histopathology), leveraging architectures like variational autoencoders (VAEs), transformers, and convolutional neural networks, often incorporating techniques like counterfactual reasoning or contrastive learning. Successful artifact disentanglement enhances the quality of data used in applications ranging from brain-computer interfaces to automated disease diagnosis, ultimately leading to more robust and reliable scientific findings and improved clinical outcomes.