@misc{
 title = {Variational inference and bayesian cnns for uncertainty estimation in multi-factorial bone age prediction},
 type = {misc},
 year = {2020},
 source = {arXiv},
 id = {31674720-f717-3211-ab8d-e49c8cfcac29},
 created = {2020-10-27T23:59:00.000Z},
 file_attached = {false},
 profile_id = {53d1e3c7-2f16-3c81-9a84-dccd45be4841},
 last_modified = {2021-05-10T03:11:28.229Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 citation_key = {Eggenreich2020},
 private_publication = {false},
 abstract = {Copyright © 2020, arXiv, All rights reserved. Additionally to the extensive use in clinical medicine, biological age (BA) in legal medicine is used to assess unknown chronological age (CA) in applications where identification documents are not available. Automatic methods for age estimation proposed in the literature are predicting point estimates, which can be misleading without the quantification of predictive uncertainty. In our multi-factorial age estimation method from MRI data, we used the Variational Inference approach to estimate the uncertainty of a Bayesian CNN model. Distinguishing model uncertainty from data uncertainty, we interpreted data uncertainty as biological variation, i.e. the range of possible CA of subjects having the same BA.},
 bibtype = {misc},
 author = {Eggenreich, S. and Payer, C. and Urschler, M. and Štern, D.}
}

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