Data Harmonization

Data harmonization aims to reconcile inconsistencies across datasets originating from different sources, a crucial step for robust data analysis and machine learning, especially in medical imaging and natural language processing. Current research emphasizes developing and benchmarking harmonization techniques, focusing on algorithms like ComBat and its extensions, generative adversarial networks (GANs), and federated learning approaches to address privacy concerns while maintaining data integrity. Effective data harmonization is vital for improving the reliability and generalizability of machine learning models, enabling more powerful analyses and potentially leading to improved diagnostic tools and treatments in healthcare and other fields.