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Oct 4, 2024 · A novel, flexible framework named longitudinally consistent triplet disentanglement autoencoder to predict an individualized longitudinal cortical ...
This paper introduces a novel framework for predicting individualized infant cortical morphological development. Distinguished from related works, it maintains ...
Experiments on predicting longitudinal infant cortical property maps validate the superior lon- gitudinal consistency and exactness of our results compared to ...
Oct 7, 2024 · A novel, flexible framework named longitudinally consistent triplet disentanglement autoencoder to predict an individualized longitudinal cortical ...
We propose a novel framework that unprecedentedly solves the problem of predicting the dynamic evolution of infant cortical surface shape solely from a single ...
Oct 31, 2024 · During the early postnatal period, the human brain undergoes rapid and dynamic development. Over the past decades, there has been increased ...
We pioneered the first prediction model for longitudinal developing cortical surfaces in infants using a spatiotemporal current-based learning framework.
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What is the developmental topography of cortical thickness during infancy?
We first developed growth models for typical cortical regions using ML-assisted symbolic regression, based on longitudinal data from over 1,000 infant MRI scans ...
Oct 24, 2024 · 22 Longitudinally Consistent Individualized. Prediction of Infant Cortical. Morphological Development. Xinrui Yuan, PhD. 23 Expanding the ...
In this paper, prediction of multiple future cognitive scores with incomplete longitudinal imaging data is modeled into a multi-task machine learning framework.
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