Data Augmentation in Histopathological Classification: An Analysis Exploring GANs with XAI and Vision Transformers

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversity of Bologna-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Federal de Uberlândia (UFU)-
Autor(es): dc.creatorRozendo, Guilherme Botazzo-
Autor(es): dc.creatorGarcia, Bianca Lançoni de Oliveira-
Autor(es): dc.creatorBorgue, Vinicius Augusto Toreli-
Autor(es): dc.creatorLumini, Alessandra-
Autor(es): dc.creatorTosta, Thaína Aparecida Azevedo-
Autor(es): dc.creatorNascimento, Marcelo Zanchetta do-
Autor(es): dc.creatorNeves, Leandro Alves-
Data de aceite: dc.date.accessioned2025-08-21T23:44:01Z-
Data de disponibilização: dc.date.available2025-08-21T23:44:01Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-09-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/app14188125-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/303070-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/303070-
Descrição: dc.descriptionGenerative adversarial networks (GANs) create images by pitting a generator (G) against a discriminator (D) network, aiming to find a balance between the networks. However, achieving this balance is difficult because G is trained based on just one value representing D’s prediction, and only D can access image features. We introduce a novel approach for training GANs using explainable artificial intelligence (XAI) to enhance the quality and diversity of generated images in histopathological datasets. We leverage XAI to extract feature information from D and incorporate it into G via the loss function, a unique strategy not previously explored in this context. We demonstrate that this approach enriches the training with relevant information and promotes improved quality and more variability in the artificial images, decreasing the FID by up to 32.7% compared to traditional methods. In the data augmentation task, these images improve the classification accuracy of Transformer models by up to 3.81% compared to models without data augmentation and up to 3.01% compared to traditional GAN data augmentation. The Saliency method provides G with the most informative feature information. Overall, our work highlights the potential of XAI for enhancing GAN training and suggests avenues for further exploration in this field.-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, SP-
Descrição: dc.descriptionDepartment of Computer Science and Engineering (DISI) University of Bologna, Via dell’ Università, 50-
Descrição: dc.descriptionScience and Technology Institute Federal University of São Paulo (UNIFESP), Avenida Cesare Mansueto Giulio Lattes, 1201, SP-
Descrição: dc.descriptionFaculty of Computer Science (FACOM) Federal University of Uberlândia (UFU), Avenida João Naves de Ávila, 2121, Bl.BMG-
Descrição: dc.descriptionDepartment of Computer Science and Statistics (DCCE) São Paulo State University (UNESP), Rua Cristóvão Colombo, 2265, SP-
Idioma: dc.languageen-
Relação: dc.relationApplied Sciences (Switzerland)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectdata augmentation-
Palavras-chave: dc.subjectexplainable artificial intelligence-
Palavras-chave: dc.subjectGAN training-
Palavras-chave: dc.subjectgenerative adversarial networks-
Palavras-chave: dc.subjecthistopathological classification-
Palavras-chave: dc.subjectvision transformers-
Título: dc.titleData Augmentation in Histopathological Classification: An Analysis Exploring GANs with XAI and Vision Transformers-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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