Implicit Neural Distance Representation for Unsupervised and Supervised Classification of Complex Anatomies
Kristine Aavild Juhl, Xabier Morales, Oscar Camara, Ole de Backer and Rasmus Reinhold Paulsen
9/21/2021
Keywords: Human (Body), Human (Head), Science & Engineering, Classification, Global Conditioning
Venue: MICCAI 2021
Bibtex:
@inproceedings{juhl2021implicit,
author = {Kristine Aavild Juhl and Xabier Morales and Ole de Backer and Oscar Camara and Rasmus Reinhold Paulsen},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
organization = {Springer},
pages = {405--415},
year = {2021},
title = {Implicit Neural Distance Representation for Unsupervised and Supervised Classification of Complex Anatomies},
entrytype = {inproceedings},
id = {juhl2021implicit}
}
Abstract
The task of 3D shape classification is closely related to finding a good representation of the shapes. In this study, we focus on surface representations of complex anatomies and on how such representations can be utilized for super-and unsupervised classification. We present a novel Implicit Neural Distance Representation based on unsigned distance fields (UDFs). The UDFs can be embedded into a low-dimensional latent space, which is optimized using only the shape itself. We demonstrate that this self-optimized latent space
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