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Found 4 papers, 1 papers with code
FIESTA: Autoencoders for accurate fiber segmentation in tractography
Our proposed method improves bundle segmentation coverage by recovering hard-to-track bundles with generative sampling through the latent space seeding of the subject bundle and the atlas bundle.
Neural Teleportation
In this paper, we explore a process called neural teleportation, a mathematical consequence of applying quiver representation theory to neural networks.
Filtering in tractography using autoencoders (FINTA)
Current brain white matter fiber tracking techniques show a number of problems, including: generating large proportions of streamlines that do not accurately describe the underlying anatomy; extracting streamlines that are not supported by the underlying diffusion signal; and under-representing some fiber populations, among others.
Structured Pruning of Neural Networks with Budget-Aware Regularization
Pruning methods have shown to be effective at reducing the size of deep neural networks while keeping accuracy almost intact.
