@book{
 title = {Uncertainty Estimation in Landmark Localization Based on Gaussian Heatmaps},
 type = {book},
 year = {2020},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 keywords = {Landmark localization,Uncertainty estimation},
 volume = {12443 LNCS},
 id = {856aa1c9-8376-388e-8ee7-b5aa8276f444},
 created = {2020-10-26T23:59:00.000Z},
 file_attached = {false},
 profile_id = {53d1e3c7-2f16-3c81-9a84-dccd45be4841},
 last_modified = {2021-05-10T03:33:14.698Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Payer2020a},
 private_publication = {false},
 abstract = {© 2020, Springer Nature Switzerland AG. In landmark localization, due to ambiguities in defining their exact position, landmark annotations may suffer from both large inter- and intra-observer variabilites, which result in uncertain annotations. Therefore, predicting a single coordinate for a landmark is not sufficient for modeling the distribution of possible landmark locations. We propose to learn the Gaussian covariances of target heatmaps, such that covariances for pointed heatmaps correspond to more certain landmarks and covariances for flat heatmaps to more uncertain or ambiguous landmarks. By fitting Gaussian functions to the predicted heatmaps, our method is able to obtain landmark location distributions, which model location uncertainties. We show on a dataset of left hand radiographs and on a dataset of lateral cephalograms that the predicted uncertainties correlate with the landmark error, as well as inter-observer variabilities.},
 bibtype = {book},
 author = {Payer, C. and Urschler, M. and Bischof, H. and Štern, D.},
 doi = {10.1007/978-3-030-60365-6_5}
}

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