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Abstract #3806

Learning White Matter Streamline Representations Using Transformer-based Siamese Networks with Triplet Margin Loss

Shenjun Zhong1, Zhaolin Chen1, and Gary Egan1
1Monash Biomedical Imaging, Monash University, Australia, Melbourne, Australia


Robust latent representation of white matter streamlines are critical for parcellating streamlines. This work introduced a novel transformer-based siamese network with triplet margin loss, that learns to embed any lengths of streamlines into fixed-length latent representations. Results showed that a minimum of two layers of transformer encoders were sufficient to model streamlines with a very limited number of training data.

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