Unsupervised anomaly detection in 2D radiographs using generative models

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dc.contributor.advisorMora Colque, Rensso
dc.contributor.authorEstacio Cerquin, Laura Jovani
dc.date.accessioned2023-02-08T16:06:37Z
dc.date.available2023-02-08T16:06:37Z
dc.date.issued2022
dc.description.abstractWe present a method based on a generative model for detection of anomalies such as prosthesis, implants, screws, zippers, and metals in Two-dimensional (2D) radiographs. The generative model is trained following an unsupervised fashion using clinical radiographs as well as simulated data, neither of them containing anomalies. Our approach employs a reconstruction loss and a latent space consistency loss which have the benefit of identifying similarities which are forced to reconstruct X-rays without anomalies. In order to detect images with anomalies, an anomaly score is also computed employing the reconstruction loss and the latent space consistency loss. Additionally, the Frechet distance is introduced as part of the reconstruction loss. These losses are computed between an input X-ray and the one reconstructed by the proposed generative model. Validation was performed using clinical pelvis radiographs. We achieved an Area Under the Curve (AUC) of 0.77 and 0.83 with clinical and synthetic data, respectively. The results demonstrated a good accuracy of the proposed method for detecting outliers as well as the advantage of utilizing synthetic data for the training stage.es_PE
dc.description.uriTesises_PE
dc.formatapplication/pdfes_PE
dc.identifier.other1076272
dc.identifier.urihttps://hdl.handle.net/20.500.12590/17432
dc.language.isoenges_PE
dc.publisherUniversidad Católica San Pabloes_PE
dc.publisher.countryPEes_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.subjectAnomaly Detectiones_PE
dc.subjectUnsupervised Learninges_PE
dc.subjectGenerative Adversarial Networkses_PE
dc.subjectPelvic radiographses_PE
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#1.02.01es_PE
dc.titleUnsupervised anomaly detection in 2D radiographs using generative modelses_PE
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typeinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE
renati.advisor.dni42846291
renati.advisor.orcidhttps://orcid.org/0000-0003-4734-8752es_PE
renati.author.dni46913887
renati.discipline611017es_PE
renati.jurorOchoa Luna, José Eduardoes_PE
renati.jurorCámara Chávez, Guillermoes_PE
renati.jurorMenotti, Davides_PE
renati.jurorMontoya Zegarra, Javieres_PE
renati.levelhttps://purl.org/pe-repo/renati/level#maestro
renati.typehttps://purl.org/pe-repo/renati/type#tesis
thesis.degree.disciplineCiencia de la Computaciónes_PE
thesis.degree.grantorUniversidad Católica San Pablo. Departamento de Ciencia de la Computaciónes_PE
thesis.degree.levelMaestríaes_PE
thesis.degree.nameMaestra en Ciencia de la Computaciónes_PE
thesis.degree.programEscuela Profesional de Ciencia de la Computaciónes_PE
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