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: Ultrasound sequence Transformers Diagnosis Deep learning Cad system Breast cancer
Resumen: 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.
Áreas temáticas: Medicine, general & internal Clinical biochemistry
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat
Identificador del autor: 0000-0002-1074-2441 0000-0002-0562-4205
Fecha de alta del registro: 2023-05-14
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.mdpi.com/2075-4418/12/5/1053
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Diagnostics. 12 (5):
Referencia 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), -. DOI: 10.3390/diagnostics12051053
DOI del artículo: 10.3390/diagnostics12051053
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2022
Tipo de publicación: Journal Publications
Repositori URV