Automation of breast cancer dosimetry with Vision Transformers

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dc.contributor.advisorJuliá-Díaz, Bruno
dc.contributor.advisorGallego Franco, Pedro
dc.contributor.authorMatas López, Ana
dc.date.accessioned2025-09-12T12:37:09Z
dc.date.available2025-09-12T12:37:09Z
dc.date.issued2025-06
dc.descriptionTreballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2025, Tutors: Bruno Juliá Diaz, Pedro Gallego Francoca
dc.description.abstractA hybrid model combining a Vision Transformers encoder with convolutional layers is proposed for breast cancer dosimetry. Its predictions were compared with a baseline U-Net trained under the same conditions. The results suggest improved performance in OAR metrics, while maintaining acceptable accuracy in PTV dose prediction. However, no statistically significant differences were found, so further research is still needed to explore the full potential of Transformers in radiotherapyca
dc.format.extent7 p.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2445/223121
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Matas, 2025
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceTreballs Finals de Grau (TFG) - Física
dc.subject.classificationRadiació ionitzantcat
dc.subject.classificationAcceleradors linealscat
dc.subject.classificationTreballs de fi de graucat
dc.subject.otherIonizing radiationeng
dc.subject.otherLinear acceleratorseng
dc.subject.otherBachelor's theseseng
dc.titleAutomation of breast cancer dosimetry with Vision Transformerseng
dc.typeinfo:eu-repo/semantics/bachelorThesisca

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Treballs Finals de Grau (TFG) - Física