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Transformer-Based Radiomics for Predicting Breast Tumor Malignancy Score in Ultrasonography

  • Datos identificativos

    Identificador: imarina:9385562
    Autores:
    Hassanien, Mohamed ASingh, Vivek KumarPuig, DomenecAbdel-Nasser, Mohamed
    Resumen:
    Breast cancer must be detected early to reduce the mortality rate. Ultrasound images can make it easier for the clinician to diagnose cases of dense breasts. This study presents a deep vision transformer-based approach for predicting breast cancer malignancy scores from ultrasound images. In particular, various state-of-the-art deep vision transformers such as BEiT, CaiT, Swin, XCiT, and VisFormer are adapted and trained to extract robust radiomics to classify breast tumors in ultrasound images as benign or malignant. The best-performing model is used to predict the malignancy score of each input ultrasound image. Experimental results revealed that the proposed approach achieves promising results for the detection of malignant tumors of the breast on ultrasound images.
  • Otros:

    Autor según el artículo: Hassanien, Mohamed A; Singh, Vivek Kumar; Puig, Domenec; Abdel-Nasser, Mohamed
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Palabras clave: Breast cancer Cad systems Radiomics Ultrasound imaging Vision transformer Vision transformers
    Resumen: Breast cancer must be detected early to reduce the mortality rate. Ultrasound images can make it easier for the clinician to diagnose cases of dense breasts. This study presents a deep vision transformer-based approach for predicting breast cancer malignancy scores from ultrasound images. In particular, various state-of-the-art deep vision transformers such as BEiT, CaiT, Swin, XCiT, and VisFormer are adapted and trained to extract robust radiomics to classify breast tumors in ultrasound images as benign or malignant. The best-performing model is used to predict the malignancy score of each input ultrasound image. Experimental results revealed that the proposed approach achieves promising results for the detection of malignant tumors of the breast on ultrasound images.
    Áreas temáticas: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: domenec.puig@urv.cat mohamed.abdelnasser@urv.cat
    Identificador del autor: 0000-0002-0562-4205 0000-0002-1074-2441
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://ebooks.iospress.nl/doi/10.3233/FAIA220351
    Referencia al articulo segun fuente origial: Frontiers In Artificial Intelligence And Applications. 356 298-307
    Referencia de l'ítem segons les normes APA: Hassanien, Mohamed A; Singh, Vivek Kumar; Puig, Domenec; Abdel-Nasser, Mohamed (2022). Transformer-Based Radiomics for Predicting Breast Tumor Malignancy Score in Ultrasonography. Amsterdam: IOS Press
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.3233/FAIA220351
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Proceedings Paper
  • Palabras clave:

    Artificial Intelligence
    Breast cancer
    Cad systems
    Radiomics
    Ultrasound imaging
    Vision transformer
    Vision transformers
    Artificial intelligence
    Ciências agrárias i
    Comunicació i informació
    Engenharias iii
    Engenharias iv
    General o multidisciplinar
    Información y documentación
    Interdisciplinar
    Medicina ii
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