Unsupervised detection of disturbances in 2D radiographs Estacio L. es_PE Ehlke M. es_PE Tack A. es_PE Castro E. es_PE Lamecker H. es_PE Mora R. es_PE Zachow S. es_PE 2024-05-30T23:13:38Z 2024-05-30T23:13:38Z 2021
dc.description This work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of CONCYTEC-PERU and German BMBF research campus MODAL (grant no. 3FO18501). The authors thank CiTeSoft-UNSA for the database access. The authors report no conflicts of interest.
dc.description.abstract We present a method based on a generative model for detection of disturbances such as prosthesis, screws, zippers, and metals in 2D radiographs. The generative model is trained in an unsupervised fashion using clinical radiographs as well as simulated data, none of which contain disturbances. Our approach employs a latent space consistency loss which has the benefit of identifying similarities, and is enforced to reconstruct X-rays without disturbances. In order to detect images with disturbances, an anomaly score is computed also employing the Frechet distance between the input X-ray and the reconstructed one using our generative model. Validation was performed using clinical pelvis radiographs. We achieved an AUC of 0.77 and 0.83 with clinical and synthetic data, respectively. The results demonstrated a good accuracy of our method for detecting outliers as well as the advantage of utilizing synthetic data.
dc.description.sponsorship Fondo Nacional de Desarrollo Científico y Tecnológico - Fondecyt
dc.identifier.scopus 2-s2.0-85107194158
dc.language.iso eng
dc.publisher IEEE Computer Society
dc.relation.ispartof Proceedings - International Symposium on Biomedical Imaging
dc.rights info:eu-repo/semantics/openAccess
dc.subject Generative models
dc.subject Anomaly detection es_PE
dc.title Unsupervised detection of disturbances in 2D radiographs
dc.type info:eu-repo/semantics/conferenceObject
dspace.entity.type Publication #PLACEHOLDER_PARENT_METADATA_VALUE#