Articles producció científica> Enginyeria Informàtica i Matemàtiques

FCA-Net: Adversarial Learning for Skin Lesion Segment Lion Based on Multi-Scale Features and Factorized Channel Attention

  • Dades identificatives

    Identificador: imarina:5880918
    Autors:
    Singh, Vivek KumarAbdel-Nasser, MohamedRashwan, Hatem AAkram, FarhanPandey, NidhiLalande, AlainPresles, BenoitRomani, SantiagoPuig, Domenec
    Resum:
    Skin lesion segmentation in dermoscopic images is still a challenge due to the low contrast and fuzzy boundaries of lesions. Moreover, lesions have high similarity with the healthy regions in terms of appearance. In this paper, we propose an accurate skin lesion segmentation model based on a modified conditional generative adversarial network (cGAN). We introduce a new block in the encoder of cGAN called factorized channel attention (FCA), which exploits both channel attention mechanism and residual 1-D kernel factorized convolution. The channel attention mechanism increases the discriminability between the lesion and non-lesion features by taking feature channel interdependencies into account. The 1-D factorized kernel block provides extra convolutions layers with a minimum number of parameters to reduce the computations of the higher-order convolutions. Besides, we use a multi-scale input strategy to encourage the development of filters which are scale-variant (i.e., constructing a scale-invariant representation). The proposed model is assessed on three skin challenge datasets: ISBI2016, ISBI2017, and ISIC2018. It yields competitive results when compared to several state-of-the-art methods in terms of Dice coefficient and intersection over union (IoU) score. The codes of the proposed model are publicly available at https://github.com/vivek231/Skin-Project.
  • Altres:

    Autor segons l'article: Singh, Vivek Kumar; Abdel-Nasser, Mohamed; Rashwan, Hatem A; Akram, Farhan; Pandey, Nidhi; Lalande, Alain; Presles, Benoit; Romani, Santiago; Puig, Domenec
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Abdellatif Fatahallah Ibrahim Mahmoud, Hatem / Abdelnasser Mohamed Mahmoud, Mohamed / AKRAM, FARHAN / Pandey, Nidhi / Puig Valls, Domènec Savi / Romaní Also, Santiago
    Paraules clau: Skin lesion Residual convolution Gradient vector flow Factorized kernel Dermoscopy images Conditional generative adversarial network Channel attention
    Resum: Skin lesion segmentation in dermoscopic images is still a challenge due to the low contrast and fuzzy boundaries of lesions. Moreover, lesions have high similarity with the healthy regions in terms of appearance. In this paper, we propose an accurate skin lesion segmentation model based on a modified conditional generative adversarial network (cGAN). We introduce a new block in the encoder of cGAN called factorized channel attention (FCA), which exploits both channel attention mechanism and residual 1-D kernel factorized convolution. The channel attention mechanism increases the discriminability between the lesion and non-lesion features by taking feature channel interdependencies into account. The 1-D factorized kernel block provides extra convolutions layers with a minimum number of parameters to reduce the computations of the higher-order convolutions. Besides, we use a multi-scale input strategy to encourage the development of filters which are scale-variant (i.e., constructing a scale-invariant representation). The proposed model is assessed on three skin challenge datasets: ISBI2016, ISBI2017, and ISIC2018. It yields competitive results when compared to several state-of-the-art methods in terms of Dice coefficient and intersection over union (IoU) score. The codes of the proposed model are publicly available at https://github.com/vivek231/Skin-Project.
    Àrees temàtiques: Telecommunications Materials science (miscellaneous) Materials science (all) General materials science General engineering General computer science Engineering, electrical & electronic Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Electrical and electronic engineering Computer science, information systems Computer science (miscellaneous) Computer science (all) Ciência da computação
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 21693536
    Adreça de correu electrònic de l'autor: mohamed.abdelnasser@urv.cat hatem.abdellatif@urv.cat santiago.romani@urv.cat domenec.puig@urv.cat
    Identificador de l'autor: 0000-0002-1074-2441 0000-0001-5421-1637 0000-0001-6673-9615 0000-0002-0562-4205
    Data d'alta del registre: 2024-09-21
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://ieeexplore.ieee.org/document/8832175
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Ieee Access. 7 130552-130565
    Referència de l'ítem segons les normes APA: Singh, Vivek Kumar; Abdel-Nasser, Mohamed; Rashwan, Hatem A; Akram, Farhan; Pandey, Nidhi; Lalande, Alain; Presles, Benoit; Romani, Santiago; Puig, Do (2019). FCA-Net: Adversarial Learning for Skin Lesion Segment Lion Based on Multi-Scale Features and Factorized Channel Attention. Ieee Access, 7(), 130552-130565. DOI: 10.1109/ACCESS.2019.2940418
    DOI de l'article: 10.1109/ACCESS.2019.2940418
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2019
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Computer Science (Miscellaneous),Computer Science, Information Systems,Engineering (Miscellaneous),Engineering, Electrical & Electronic,Materials Science (Miscellaneous),Telecommunications
    Skin lesion
    Residual convolution
    Gradient vector flow
    Factorized kernel
    Dermoscopy images
    Conditional generative adversarial network
    Channel attention
    Telecommunications
    Materials science (miscellaneous)
    Materials science (all)
    General materials science
    General engineering
    General computer science
    Engineering, electrical & electronic
    Engineering (miscellaneous)
    Engineering (all)
    Engenharias iv
    Engenharias iii
    Electrical and electronic engineering
    Computer science, information systems
    Computer science (miscellaneous)
    Computer science (all)
    Ciência da computação
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