Detail výsledku
Evaluating deep learning uncertainty measures in cephalometric landmark localization
Kodym Oldřich, Ing., Ph.D., UPGM (FIT)
Cephalometric analysis is a key step in the process of dental treatment
diagnosis, planning and surgery. Localization of a set of landmark points is an
important but time-consuming and subjective part of this task. Deep learning is
able to automate this process but the model predictions are usually given without
any uncertainty information which is necessary in medical applications. This work
evaluates three uncertainty measures applicable to deep learning models on the
task of cephalometric landmark localization. We compare uncertainty estimation
based on final network activation with an ensemble-based and a Bayesian-based
approach. We conduct two experiments with elastically distorted cephalogram
images and images containing undesirable horizontal skull rotation which the
models should be able to detect as unfamiliar and unsuitable for automatic
evaluation. We show that all three uncertainty measures have this detection
capability and are a viable option when landmark localization with uncertainty
estimation is required.
Landmark Localization, Cephalometric Landmarks, Deep Learning, Uncertainty
Estimation.
@inproceedings{BUT168478,
author="Dušan {Drevický} and Oldřich {Kodym}",
title="Evaluating deep learning uncertainty measures in cephalometric landmark localization",
booktitle="Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING",
year="2020",
pages="213--220",
publisher="Institute for Systems and Technologies of Information, Control and Communication",
address="Valetta",
doi="10.5220/0009375302130220",
isbn="978-989-758-398-8",
url="http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0009375302130220"
}