Mining multi-center heterogeneous medical data with distributed synthetic learning

• Published in: Nature communications Vol. 14; no. 1; pp. 5510 - 16
• Main Authors: Chang, Qi, Yan, Zhennan, Zhou, Mu, Qu, Hui, He, Xiaoxiao, Zhang, Han, Baskaran, Lohendran, Al’Aref, Subhi, Li, Hongsheng, Zhang, Shaoting, Metaxas, Dimitris N.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.09.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/2164; 631/114/2400; 692/700/1421; Angiography; Brain cancer; Brain tumors; Computed tomography; Datasets; Health care; Health care facilities; Heterogeneity; Histopathology; Humanities and Social Sciences; Image quality; Image segmentation; Learning; Medical imaging; multidisciplinary; Performance evaluation; Privacy; Science; Science (multidisciplinary); Synthetic data
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-023-40687-y

Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the healthcare system. In this study, we propose the Distributed Synthetic Learning (DSL) architecture to learn across multiple medical centers and ensure the protection of sensitive personal information. DSL enables the building of a homogeneous dataset with entirely synthetic medical images via a form of GAN-based synthetic learning. The proposed DSL architecture has the following key functionalities: multi-modality learning, missing modality completion learning, and continual learning. We systematically evaluate the performance of DSL on different medical applications using cardiac computed tomography angiography (CTA), brain tumor MRI, and histopathology nuclei datasets. Extensive experiments demonstrate the superior performance of DSL as a high-quality synthetic medical image provider by the use of an ideal synthetic quality metric called Dist-FID. We show that DSL can be adapted to heterogeneous data and remarkably outperforms the real misaligned modalities segmentation model by 55% and the temporal datasets segmentation model by 8%. Here the authors present Distributed Synthetic Learning, a system that addresses data privacy, isolated data islands, and heterogeneity concerns in healthcare analytics by learning to generate state-of-the-art synthetic data for downstream tasks.