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Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis by Dhinagar, Nikhil J.; Singh, Amit; Ozarkar, Saket; Buwa, Ketaki; Thomopoulos, Sophia I.; Owens-Walton, Conor; Laltoo, Emily; Chen, Yao-Liang; Cook, Philip; McMillan, Corey; Tsai, Chih-Chien; Wang, J-J; Wu, Yih-Ru; Thompson, Paul M. is a scholarly article available to read on EtoBox.

What is Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis about?

Transfer learning represents a recent paradigm shift in the way we build artificial intelligence (AI) systems. In contrast to training task-specific models, transfer learning involves pre-training deep learning models on a large corpus of data and minimally fine-tuning them for adaptation to specific tasks. Even so, for 3D medical imaging tasks, we do not know if it is best to pre-train models on natural images, medical images, or even synthetically generated MRI scans or video data. To evaluate these alternatives, here we benchmarked vision transformers (ViTs) and convolutional neural networks (CNNs), initialized with varied upstream pre-training approaches. These methods were then adapted to three unique downstream neuroimaging tasks with a range of difficulty: Alzheimer's disease (AD) and Parkinson's disease (PD) classification, "brain age" prediction. Experimental tests led to the following key observations: 1. Pre-training improved performance across all tasks including a boost of 7.4% for AD classification and 4.6% for PD classification for the ViT and 19.1% for PD classification and reduction in brain age prediction error by 1.26 years for CNNs, 2. Pre-training on large-scal

Author

Dhinagar, Nikhil J.; Singh, Amit; Ozarkar, Saket; Buwa, Ketaki; Thomopoulos, Sophia I.; Owens-Walton, Conor; Laltoo, Emily; Chen, Yao-Liang; Cook, Philip; McMillan, Corey; Tsai, Chih-Chien; Wang, J-J; Wu, Yih-Ru; Thompson, Paul M.

Published

2023

Language

EN