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Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification

  • Datos identificativos

    Identificador: imarina:4089619
    Autores:
    Singh, Vivek KumarRomani, SantiagoRashwan, Hatem AAkram, FarhanPandey, NidhiKamal Sarker, Md MostafaAbdulwahab, SaddamTorrents-Barrena, JordinaSaleh, AdelArquez, MiguelArenas, MeritxellPuig, Domenec
    Resumen:
    © Springer Nature Switzerland AG 2018. This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography. We hypothesized that the cGAN structure is well-suited to accurately outline the mass area, especially when the training data is limited. The generative network learns intrinsic features of tumors while the adversarial network enforces segmentations to be similar to the ground truth. Experiments performed on dozens of malignant tumors extracted from the public DDSM dataset and from our in-house private dataset confirm our hypothesis with very high Dice coefficient and Jaccard index (>94% and >89%, respectively) outperforming the scores obtained by other state-of-the-art approaches. Furthermore, in order to detect portray significant morphological features of the segmented tumor, a specific Convolutional Neural Network (CNN) have also been designed for classifying the segmented tumor areas into four types (irregular, lobular, oval and round), which provides an overall accuracy about 72% with the DDSM dataset.
  • Otros:

    Autor según el artículo: Singh, Vivek Kumar; Romani, Santiago; Rashwan, Hatem A; Akram, Farhan; Pandey, Nidhi; Kamal Sarker, Md Mostafa; Abdulwahab, Saddam; Torrents-Barrena, Jordina; Saleh, Adel; Arquez, Miguel; Arenas, Meritxell; Puig, Domenec
    Departamento: Ciències Mèdiques Bàsiques Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdulwahab, Saddam Abdulrhman Hamed / AKRAM, FARHAN / Arenas Prat, Meritxell / Puig Valls, Domènec Savi / Romaní Also, Santiago
    Palabras clave: Mass shape classification Mass segmentation Mammography Cnn Cgan Cancer molecular subtypes
    Resumen: © Springer Nature Switzerland AG 2018. This paper proposes a novel approach based on conditional Generative Adversarial Networks (cGAN) for breast mass segmentation in mammography. We hypothesized that the cGAN structure is well-suited to accurately outline the mass area, especially when the training data is limited. The generative network learns intrinsic features of tumors while the adversarial network enforces segmentations to be similar to the ground truth. Experiments performed on dozens of malignant tumors extracted from the public DDSM dataset and from our in-house private dataset confirm our hypothesis with very high Dice coefficient and Jaccard index (>94% and >89%, respectively) outperforming the scores obtained by other state-of-the-art approaches. Furthermore, in order to detect portray significant morphological features of the segmented tumor, a specific Convolutional Neural Network (CNN) have also been designed for classifying the segmented tumor areas into four types (irregular, lobular, oval and round), which provides an overall accuracy about 72% with the DDSM dataset.
    Áreas temáticas: Linguística e literatura Interdisciplinar Geociências Engenharias iv Educação Ciências ambientais Ciência da computação Arquitetura, urbanismo e design Administração pública e de empresas, ciências contábeis e turismo
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 16113349
    Direcció de correo del autor: saddam.abdulwahab@urv.cat hatem.abdellatif@urv.cat saddam.abdulwahab@urv.cat meritxell.arenas@urv.cat santiago.romani@urv.cat domenec.puig@urv.cat
    Identificador del autor: 0000-0001-5421-1637 0000-0003-0815-2570 0000-0001-6673-9615 0000-0002-0562-4205
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/submittedVersion
    Enlace a la fuente original: https://link.springer.com/chapter/10.1007/978-3-030-00934-2_92#citeas
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Automatic Evaluation Of Disclosure Risks Of Text Anonymization Methods. 11071 LNCS 833-840
    Referencia de l'ítem segons les normes APA: Singh, Vivek Kumar; Romani, Santiago; Rashwan, Hatem A; Akram, Farhan; Pandey, Nidhi; Kamal Sarker, Md Mostafa; Abdulwahab, Saddam; Torrents-Barrena, (2018). Conditional generative adversarial and convolutional networks for X-ray breast mass segmentation and shape classification. Heidelberg: Springer Berlin Heidelberg
    DOI del artículo: 10.1007/978-3-030-00934-2_92
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2018
    Tipo de publicación: Proceedings Paper
  • Palabras clave:

    Mass shape classification
    Mass segmentation
    Mammography
    Cnn
    Cgan
    Cancer molecular subtypes
    Linguística e literatura
    Interdisciplinar
    Geociências
    Engenharias iv
    Educação
    Ciências ambientais
    Ciência da computação
    Arquitetura, urbanismo e design
    Administração pública e de empresas, ciências contábeis e turismo
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