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

  • Dades identificatives

    Identificador: imarina:4089619
    Autors:
    Singh, Vivek KumarRomani, SantiagoRashwan, Hatem AAkram, FarhanPandey, NidhiKamal Sarker, Md MostafaAbdulwahab, SaddamTorrents-Barrena, JordinaSaleh, AdelArquez, MiguelArenas, MeritxellPuig, Domenec
    Resum:
    © 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.
  • Altres:

    Autor segons l'article: 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
    Departament: Ciències Mèdiques Bàsiques Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdulwahab, Saddam Abdulrhman Hamed / AKRAM, FARHAN / Arenas Prat, Meritxell / Puig Valls, Domènec Savi / Romaní Also, Santiago
    Paraules clau: Mass shape classification Mass segmentation Mammography Cnn Cgan Cancer molecular subtypes
    Resum: © 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.
    Àrees temàtiques: 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
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 16113349
    Adreça de correu electrònic de l'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 de l'autor: 0000-0001-5421-1637 0000-0003-0815-2570 0000-0001-6673-9615 0000-0002-0562-4205
    Data d'alta del registre: 2024-10-12
    Versió de l'article dipositat: info:eu-repo/semantics/submittedVersion
    Enllaç font original: https://link.springer.com/chapter/10.1007/978-3-030-00934-2_92#citeas
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Automatic Evaluation Of Disclosure Risks Of Text Anonymization Methods. 11071 LNCS 833-840
    Referència 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 de l'article: 10.1007/978-3-030-00934-2_92
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2018
    Tipus de publicació: Proceedings Paper
  • Paraules clau:

    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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