Investigation of distributed learning for automated lesion detection in head MR images.
Radiol Phys Technol
; 17(3): 725-738, 2024 Sep.
Article
em En
| MEDLINE
| ID: mdl-39048847
ABSTRACT
In this study, we investigated the application of distributed learning, including federated learning and cyclical weight transfer, in the development of computer-aided detection (CADe) software for (1) cerebral aneurysm detection in magnetic resonance (MR) angiography images and (2) brain metastasis detection in brain contrast-enhanced MR images. We used datasets collected from various institutions, scanner vendors, and magnetic field strengths for each target CADe software. We compared the performance of multiple strategies, including a centralized strategy, in which software development is conducted at a development institution after collecting de-identified data from multiple institutions. Our results showed that the performance of CADe software trained through distributed learning was equal to or better than that trained through the centralized strategy. However, the distributed learning strategies that achieved the highest performance depend on the target CADe software. Hence, distributed learning can become one of the strategies for CADe software development using data collected from multiple institutions.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Imageamento por Ressonância Magnética
/
Aneurisma Intracraniano
Limite:
Humans
Idioma:
En
Revista:
Radiol Phys Technol
Assunto da revista:
BIOFISICA
/
RADIOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Japão