Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution.
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%0 Conference Paper
%1 conf/miccai/RachmadiHMWMS21
%A Rachmadi, Muhammad Febrian
%A del C. Valdés Hernández, Maria
%A Maulana, Rizal
%A Wardlaw, Joanna M.
%A Makin, Stephen D.
%A Skibbe, Henrik
%B PRIME@MICCAI
%D 2021
%E Rekik, Islem
%E Adeli, Ehsan
%E Park, Sang Hyun
%E Schnabel, Julia A.
%I Springer
%K dblp
%P 168-180
%T Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution.
%U http://dblp.uni-trier.de/db/conf/miccai/prime2021.html#RachmadiHMWMS21
%V 12928
%@ 978-3-030-87602-9
@inproceedings{conf/miccai/RachmadiHMWMS21,
added-at = {2022-10-02T00:00:00.000+0200},
author = {Rachmadi, Muhammad Febrian and del C. Valdés Hernández, Maria and Maulana, Rizal and Wardlaw, Joanna M. and Makin, Stephen D. and Skibbe, Henrik},
biburl = {https://www.bibsonomy.org/bibtex/289157fd6f5b3c480e2e4d82a03eb219a/dblp},
booktitle = {PRIME@MICCAI},
crossref = {conf/miccai/2021prime},
editor = {Rekik, Islem and Adeli, Ehsan and Park, Sang Hyun and Schnabel, Julia A.},
ee = {https://doi.org/10.1007/978-3-030-87602-9_16},
interhash = {c2d3ad9faa5e4fc0a47fa9a951e972c6},
intrahash = {89157fd6f5b3c480e2e4d82a03eb219a},
isbn = {978-3-030-87602-9},
keywords = {dblp},
pages = {168-180},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
timestamp = {2024-04-09T22:24:57.000+0200},
title = {Probabilistic Deep Learning with Adversarial Training and Volume Interval Estimation - Better Ways to Perform and Evaluate Predictive Models for White Matter Hyperintensities Evolution.},
url = {http://dblp.uni-trier.de/db/conf/miccai/prime2021.html#RachmadiHMWMS21},
volume = 12928,
year = 2021
}