LiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorGraduate Program of Informatics-
Autor(es): dc.contributorRegensburg Medical Image Computing (ReMIC)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorSouza, Luis A.-
Autor(es): dc.creatorPacheco, Andre G. C.-
Autor(es): dc.creatorDe Angelo, Gabriel G.-
Autor(es): dc.creatorOliveira-Santos, Thiago-
Autor(es): dc.creatorPalm, Christoph-
Autor(es): dc.creatorPapa, Joao P.-
Data de aceite: dc.date.accessioned2025-08-21T23:25:29Z-
Data de disponibilização: dc.date.available2025-08-21T23:25:29Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/SIBGRAPI62404.2024.10716324-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/305260-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/305260-
Descrição: dc.descriptionSkin cancer is the most common type of cancer in the world, accounting for approximately 30% of all diagnosed tumors. Early diagnosis reduces mortality rates and prevents disfiguring effects in different body regions. In recent years, machine learning techniques, particularly deep learning, have shown promising results in this task, presenting studies that have demonstrated that combining a patient's clinical information with images of the lesion is crucial for improving the classification of skin lesions. Despite that, meaningful use of clinical information with multiple images is mandatory, requiring further investigation. Thus, this project aims to contribute to developing multimodal machine learning-based models to cope with the skin lesion classification task employing a lightweight transformer model. As a main hypothesis, models can take multiple images from different sources as input, along with clinical information from the patient's history, leading to a more reliable diagnosis. Our model deals with the not-trivial task of combining images and clinical information (from anamneses) concerning the skin lesions in a lightweight transformer architecture that does not demand high computation resources but still presents competitive classification results.-
Descrição: dc.descriptionFederal University of Espírito Santo Graduate Program of Informatics-
Descrição: dc.descriptionOTH Regensburg Regensburg Medical Image Computing (ReMIC)-
Descrição: dc.descriptionSão Paulo State Univesity Department of Computing-
Descrição: dc.descriptionSão Paulo State Univesity Department of Computing-
Idioma: dc.languageen-
Relação: dc.relationBrazilian Symposium of Computer Graphic and Image Processing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectLightweight Architectures-
Palavras-chave: dc.subjectSkin Lesion Detection-
Palavras-chave: dc.subjectTransformers-
Título: dc.titleLiwTERM: A Lightweight Transformer-Based Model for Dermatological Multimodal Lesion Detection-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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