Imaging Biomarkers

Imaging biomarkers leverage medical images to extract quantitative features predictive of disease or treatment response, aiming to improve diagnosis, prognosis, and personalized medicine. Current research focuses on developing and validating automated methods for biomarker extraction using deep learning models (e.g., convolutional neural networks, hypergraph models) and advanced image processing techniques, often addressing challenges like motion correction and bias mitigation. This field holds significant promise for non-invasive disease monitoring and improved clinical decision-making across various conditions, from sepsis and cancer to neurological disorders like multiple sclerosis and autism spectrum disorder.