Year: 2023
Type: article
Abstract: 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%.
Source: Nature Communications
Institutions: Rutgers, The State University of New Jersey, Shanghai Artificial Intelligence Laboratory, National University of Singapore, National University Heart Centre Singapore, Duke-NUS Medical School, National Heart Centre Singapore, University of Arkansas for Medical Sciences, Chinese University of Hong Kong, Centre for Perceptual and Interactive Intelligence, Sensetime (China)
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $6,290
Awards: SCC-IRG Track 2: Making Micromobility Smarter and Safer (M2S2), Collaborative Research: HCC: Medium: Linguistically-Driven Sign Recognition from Continuous Signing for American Sign Language (ASL), CHS: Medium: Data Driven Biomechanically Accurate Modeling of Human Gait on Unconstrained Terrain, AitF: Collaborative Research: Topological Algorithms for 3D/4D Cardiac Images: Understanding Complex and Dynamic Structures, NSF Convergence Accelerator Track H: AI-based Tools to Enhance Access and Opportunities for the Deaf, G4186481584, S&AS: FND: COLLAB: Adaptable Vehicular Sensing and Control for Fleet-Oriented Systems in Smart Cities, Phase 1 IUCRC Rutgers-New Brunswick: Center for Accelerated Real Time Analytics (CARTA)
OpenAlex ID: W4386526397