Categorical and phenotypic image synthetic learning as an alternative to federated learning

Multi-center collaborations are crucial in developing robust and generalizable machine learning models in medical imaging. Traditional methods, such as centralized data sharing or federated learning (FL), face challenges, including privacy issues, communication burdens, and synchronization complexities. We present CATegorical and PHenotypic Image SyntHetic learnING (CATphishing), an alternative to FL using Latent Diffusion Models (LDM) to generate synthetic multi-contrast three-dimensional magnetic resonance imaging data for downstream tasks, eliminating the need for raw data sharing or iterative inter-site communication. Each institution trains an LDM to capture site-specific data distributions, producing synthetic samples aggregated at a central server. We evaluate CATphishing using data from 2491 patients across seven institutions for isocitrate dehydrogenase mutation classification and three-class tumor-type classification. CATphishing achieves accuracy comparable to centralized training and FL, with synthetic data exhibiting high fidelity. This method addresses privacy, scalability, and communication challenges, offering a promising alternative for collaborative artificial intelligence development in medical imaging.

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Publication Details

Journal
Nature Communications
Published
2025-10-23
DOI
https://doi.org/10.1038/s41467-025-64385-z
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

Categorical and phenotypic image synthetic learning as an alternative to federated learning

Richard J. Bruce, Kimmo J. Hatanpaa, Rajan Jain, James M. Holcomb et al.
Nature Communications
Radiomics and Machine Learning in Medical Imaging
article

Categorical and phenotypic image synthetic learning as an alternative to federated learning

Richard J. Bruce, Kimmo J. Hatanpaa, Rajan Jain, James M. Holcomb, Ananth J. Madhuranthakam, Divya D. Reddy, Niloufar Saadat, Marco C. Pinho, Chandan Ganesh Bangalore Yogananda, Jason Bowerman, Toral Patel, Baowei Fei, Joseph A. Maldjian, Benjamin Wagner, Matthew Lee, Nghi Cong Dung Truong
article en

Abstract

Multi-center collaborations are crucial in developing robust and generalizable machine learning models in medical imaging. Traditional methods, such as centralized data sharing or federated learning (FL), face challenges, including privacy issues, communication burdens, and synchronization complexities. We present CATegorical and PHenotypic Image SyntHetic learnING (CATphishing), an alternative to FL using Latent Diffusion Models (LDM) to generate synthetic multi-contrast three-dimensional magnetic resonance imaging data for downstream tasks, eliminating the need for raw data sharing or iterative inter-site communication. Each institution trains an LDM to capture site-specific data distributions, producing synthetic samples aggregated at a central server. We evaluate CATphishing using data from 2491 patients across seven institutions for isocitrate dehydrogenase mutation classification and three-class tumor-type classification. CATphishing achieves accuracy comparable to centralized training and FL, with synthetic data exhibiting high fidelity. This method addresses privacy, scalability, and communication challenges, offering a promising alternative for collaborative artificial intelligence development in medical imaging.

Nature CommunicationsVol. 16(1)
University of Wisconsin–Madison (US), The University of Texas at Dallas (US), Southwestern Medical Center (US), Mayo Clinic in Arizona (US), Neurological Surgery (US), New York University (US), The University of Texas Southwestern Medical Center (US)
National Institutes of Health, National Cancer Institute
Openalex Percentile: Top 55%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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