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Abstract #0876

NERVE: Neuroimaging Embedding Representation via Variational Encoding

Pei-Shin Chen1, Teng-Yi Huang1, Yi-Ru Lin2, Tzu-Chao Chuang3, and Hsiao-Wen Chung4
1Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, 2Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, 3Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan, 4Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan

Synopsis

Keywords: Alzheimer's Disease, Dementia, Variational Autoencoder, Convolutional Neural Networks

Motivation: Inspired by large language models, we aim to apply embedding techniques to tackle high-dimensional MRI data and limited labels, aiming to improve Alzheimer's disease classification.

Goal(s): Develop an open, general-purpose brain MRI embedding model using variational autoencoders to create informative embeddings for downstream neuroimaging tasks.

Approach: Collected large MRI datasets, extracted hippocampus and amygdala patches, trained VAEs to generate embeddings (NERVE), and used these embeddings to train a ResNet classifier for AD detection.

Results: NERVE embeddings significantly improved AD classification, with the open dataset model achieving an F1-score of 0.92, outperforming models using raw or preprocessed MRI data.

Impact: NERVE encodes brain MRI into a compressed embed. A generalized and open-source NERVE model offers broad applications in neuroscience.

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