Volumetric Color-Texture Representation for Colorectal Polyp Classification in Histopathology Images

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorFares, Ricardo T.-
Autor(es): dc.creatorRibas, Lucas C.-
Data de aceite: dc.date.accessioned2025-08-21T16:11:21Z-
Data de disponibilização: dc.date.available2025-08-21T16:11:21Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.5220/0013315800003912-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/300034-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/300034-
Descrição: dc.descriptionWith the growth of real-world applications generating numerous images, analyzing color-texture information has become essential, especially when spectral information plays a key role. Currently, many randomized neural network texture-based approaches were proposed to tackle color-textures. However, they are integrative approaches or fail to achieve competitive processing time. To address these limitations, this paper proposes a single-parameter color-texture representation that captures both spatial and spectral patterns by sliding volumetric (3D) color cubes over the image and encoding them with a Randomized Autoencoder (RAE). The key idea of our approach is that simultaneously encoding both color and texture information allows the autoencoder to learn meaningful patterns to perform the decoding operation. Hence, we employ as representation the flattened decoder’s learned weights. The proposed approach was evaluated in three color-texture benchmark datasets: USPtex, Outex, and MBT. We also assessed our approach in the challenging and important application of classifying colorectal polyps. The results show that the proposed approach surpasses many literature methods, including deep convolutional neural networks. Therefore, these findings indicate that our representation is discriminative, showing its potential for broader applications in histological images and pattern recognition tasks.-
Descrição: dc.descriptionSão Paulo State University Institute of Biosciences Humanities and Exact Sciences, SP-
Descrição: dc.descriptionSão Paulo State University Institute of Biosciences Humanities and Exact Sciences, SP-
Formato: dc.format210-221-
Idioma: dc.languageen-
Relação: dc.relationProceedings of the International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications-
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Palavras-chave: dc.subjectColor-Texture-
Palavras-chave: dc.subjectRandomized Neural Network-
Palavras-chave: dc.subjectTexture Representation Learning-
Título: dc.titleVolumetric Color-Texture Representation for Colorectal Polyp Classification in Histopathology Images-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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