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Qualitative analysis through visual interpretability techniques of neural network models for mammography classification

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dc.contributor.authorRodríguez Sampayo, Marta
dc.contributor.directorRincón Zamorano, Mariano
dc.date.accessioned2024-05-20T12:39:05Z
dc.date.available2024-05-20T12:39:05Z
dc.date.issued2021-09-01
dc.description.abstractNowadays, research in the field of artificial intelligence is focusing on the explainability of the developed algorithms, mainly neural networks. This trend is known as XAI and brings certain advantages such as increased confidence in the decision-making process, improved capacity for error analysis, verification of results and possibility of model refinement, among others. In this work we have focused on interpreting the predictions of recently developed deep learning models through different visualization techniques. The use case we introduce is the detection of breast cancer through the classification of mammographies, since the medical field is widely benefited by the contributions of XAI methods. Furthermore, the target neural networks are based on recent and poorly explored architectures. These are the Vision Transformer model, built through attention blocks, and EfficientNet, designed to improve the performance of convolutional networks.en
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/14675
dc.language.isoen
dc.publisherUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.degreeMáster universitario en Investigación en Inteligencia Artificial
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsExplainable Artificial Intelligence
dc.subject.keywordsinterpretability
dc.subject.keywordsDeep Learning
dc.subject.keywordsEfficientNet
dc.subject.keywordsvision transformer
dc.subject.keywordsmammography
dc.titleQualitative analysis through visual interpretability techniques of neural network models for mammography classificationes
dc.typetesis de maestríaes
dc.typemaster thesisen
dspace.entity.typePublication
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