High Dimensional Imaging

High-dimensional imaging focuses on efficiently processing and analyzing extremely large datasets from various imaging modalities, aiming to improve resolution, speed, and interpretability. Current research emphasizes developing novel model architectures, including deep unrolling networks, transformer-based models, and explainable boosting machines, often incorporating dimensionality reduction techniques like operator sketching and subspace modeling to handle computational challenges. These advancements are significantly impacting fields like medical imaging (e.g., fMRI, MRI, X-ray CT) and cell analysis, enabling faster diagnoses, more accurate classifications, and improved understanding of complex biological processes.