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Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences

  • Dades identificatives

    Identificador: imarina:9265244
    Autors:
    Hassanien, Mohamed ASingh, Vivek KumarPuig, DomenecAbdel-Nasser, Mohamed
    Resum:
    Breast cancer needs to be detected early to reduce mortality rate. Ultrasound imaging (US) could significantly enhance diagnosing cases with dense breasts. Most of the existing computer-aided diagnosis (CAD) systems employ a single ultrasound image for the breast tumor to extract features to classify it as benign or malignant. However, the accuracy of such CAD system is limited due to the large tumor size and shape variation, irregular and ambiguous tumor boundaries, and low signal-to-noise ratio in ultrasound images due to their noisy nature and the significant similarity between normal and abnormal tissues. To handle these issues, we propose a deep-learning-based radiomics method based on breast US sequences in this paper. The proposed approach involves three main components: radiomic features extraction based on a deep learning network, so-called ConvNeXt, a malignancy score pooling mechanism, and visual interpretations. Specifically, we employ the ConvNeXt network, a deep convolutional neural network (CNN) trained using the vision transformer style. We also propose an efficient pooling mechanism to fuse the malignancy scores of each breast US sequence frame based on image-quality statistics. The ablation study and experimental results demonstrate that our method achieves competitive results compared to other CNN-based methods.
  • Altres:

    Autor segons l'article: Hassanien, Mohamed A; Singh, Vivek Kumar; Puig, Domenec; Abdel-Nasser, Mohamed
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Paraules clau: Ultrasound sequence Transformers Diagnosis Deep learning Cad system Breast cancer
    Resum: Breast cancer needs to be detected early to reduce mortality rate. Ultrasound imaging (US) could significantly enhance diagnosing cases with dense breasts. Most of the existing computer-aided diagnosis (CAD) systems employ a single ultrasound image for the breast tumor to extract features to classify it as benign or malignant. However, the accuracy of such CAD system is limited due to the large tumor size and shape variation, irregular and ambiguous tumor boundaries, and low signal-to-noise ratio in ultrasound images due to their noisy nature and the significant similarity between normal and abnormal tissues. To handle these issues, we propose a deep-learning-based radiomics method based on breast US sequences in this paper. The proposed approach involves three main components: radiomic features extraction based on a deep learning network, so-called ConvNeXt, a malignancy score pooling mechanism, and visual interpretations. Specifically, we employ the ConvNeXt network, a deep convolutional neural network (CNN) trained using the vision transformer style. We also propose an efficient pooling mechanism to fuse the malignancy scores of each breast US sequence frame based on image-quality statistics. The ablation study and experimental results demonstrate that our method achieves competitive results compared to other CNN-based methods.
    Àrees temàtiques: Medicine, general & internal Internal medicine Clinical biochemistry
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat
    Identificador de l'autor: 0000-0002-1074-2441 0000-0002-0562-4205
    Data d'alta del registre: 2024-10-12
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.mdpi.com/2075-4418/12/5/1053
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Diagnostics. 12 (5): 1053-
    Referència de l'ítem segons les normes APA: Hassanien, Mohamed A; Singh, Vivek Kumar; Puig, Domenec; Abdel-Nasser, Mohamed (2022). Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences. Diagnostics, 12(5), 1053-. DOI: 10.3390/diagnostics12051053
    DOI de l'article: 10.3390/diagnostics12051053
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2022
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Clinical Biochemistry,Medicine, General & Internal
    Ultrasound sequence
    Transformers
    Diagnosis
    Deep learning
    Cad system
    Breast cancer
    Medicine, general & internal
    Internal medicine
    Clinical biochemistry
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