Mining multi-center heterogeneous medical data with distributed synthetic learning.
Nat Commun
; 14(1): 5510, 2023 09 07.
Article
in En
| MEDLINE
| ID: mdl-37679325
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%.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Brain Neoplasms
/
Learning
Type of study:
Clinical_trials
Limits:
Humans
Language:
En
Journal:
Nat Commun
Journal subject:
BIOLOGIA
/
CIENCIA
Year:
2023
Document type:
Article
Affiliation country:
United States