Meeting Banner
Abstract #0879

A brain-age prediction model in 3D-CNN-ViT deep learning network

Shuoqiu Gan1, Chenhao Fang2, Xiaoyu Xu2, Ruipeng Xu1, Jun Huang1, Dengdi Sun3, and Qing He4
1Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China, 2AHU-IAI AI Joint Laboratory, Anhui University, Hefei, China, 3Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Artificial Intelligence, Anhui University, Hefei, China, 4State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

Synopsis

Keywords: Aging, Aging, Brain age prediction

Motivation: The CNN-ViT model, equipped with CNN and vision transformer (ViT) branches, can capture and then fuse local features and global representations of images, potentially improving the performance of brain-age prediction.

Goal(s): To assess the performance of brain-age prediction model in CNN-ViT architecture.

Approach: The 3D-CNN-ViT brain-age model was trained with T1 weighted images, followed by two comparative experiments and a clinical experiment to assess its performance superiority.

Results: The CNN-ViT brain-age model outperformed both the CNN and ViT one. Besides the whole brain, this architecture also effectively predicted brain-age in individual lobes, where the brain-aging acceleration was more sensitive to dementia.

Impact: The fused local and global features of MRI data improve the performance in brain-age prediction paradigm, suggesting that the CNN-ViT architecture has potential to promote prognosis prediction or biotype classification in clinical applications using MRI data.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords