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

  • Identification data

    Identifier: imarina:9385562
    Handle: https://hdl.handle.net/20.500.11797/imarina9385562
    Authors:
    Hassanien, Mohamed ASingh, Vivek KumarPuig, DomenecAbdel-Nasser, Mohamed
    Abstract:
    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.

  • Others:

    Author, as appears in the article.: Hassanien, Mohamed A; Singh, Vivek Kumar; Puig, Domenec; Abdel-Nasser, Mohamed
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Keywords: Breast cancer Cad systems Radiomics Ultrasound imaging Vision transformer Vision transformers
    Abstract: 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.
    Thematic Areas: Artificial intelligence Ciências agrárias i Comunicació i informació Engenharias iii Engenharias iv General o multidisciplinar Información y documentación Interdisciplinar Medicina ii
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: domenec.puig@urv.cat mohamed.abdelnasser@urv.cat
    Author identifier: 0000-0002-0562-4205 0000-0002-1074-2441
    Record's date: 2024-10-12
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://ebooks.iospress.nl/doi/10.3233/FAIA220351
    Papper original source: Frontiers In Artificial Intelligence And Applications. 356 298-307
    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
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Article's DOI: 10.3233/FAIA220351
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2022
    Publication Type: Proceedings Paper

  • Keywords:

    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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