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Abstract #0559

Generalize diffusion-MRI-based brain age predictive model using transfer learning

Chang-Le Chen1, Yung-Chin Hsu2, and Wen-Yih Isaac Tseng1,3,4

1Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan, 2AcroViz Technology Inc., Taipei, Taiwan, 3Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan, 4Molecular Imaging Center, National Taiwan University, Taipei, Taiwan

Heterogeneity of diffusion MRI data limits the diffusion-MRI-based machine learning model to be generalized to the data acquired at other sites. To generalize the brain age model based on diffusion-MRI-derived features, we used transfer learning techniques to transfer the pre-trained model from the source domain to the target domain with a few tuning data. We found that 75 tuning data with transfer learning framework achieved the acceptable performance, and 150 tuning data achieved the performance comparable to the maximum samples in the target domain. This study provides a practical solution to solve the limitation of diffusion-MRI-based model using transfer learning.

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