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Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models

A new deep learning approach to learn a hierarchy of conditional latent variables that models a population of anatomical segmentations of interest, enables the classification of distinct clinical conditions, and visualizes the anatomical variability encoded by the learned latent space.

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Cite this as

Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni, Wenjia Bai, Antonio de Marvao, Ozan Oktay, Christian Ledig, Loic Le Folgoc, Konstantinos Kamnitsas, Georgia Doumou, Jinming Duan, Sanjay K. Prasad, Stuart A. Cook, Declan P. O’Regan, Daniel Rueckert (2024). Dataset: Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models. https://doi.org/10.57702/0ksa9lcn

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Additional Info

Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.1907.00058
Author Carlo Biffi
More Authors
Juan J. Cerrolaza
Giacomo Tarroni
Wenjia Bai
Antonio de Marvao
Ozan Oktay
Christian Ledig
Loic Le Folgoc
Konstantinos Kamnitsas
Georgia Doumou
Jinming Duan
Sanjay K. Prasad
Stuart A. Cook
Declan P. O’Regan
Daniel Rueckert
Homepage https://doi.org/10.5281/zenodo.3247898