FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention
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 modifie...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8832175/ |
| _version_ | 1818947311164719104 |
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| author | Vivek Kumar Singh Mohamed Abdel-Nasser Hatem A. Rashwan Farhan Akram Nidhi Pandey Alain Lalande Benoit Presles Santiago Romani Domenec Puig |
| author_facet | Vivek Kumar Singh Mohamed Abdel-Nasser Hatem A. Rashwan Farhan Akram Nidhi Pandey Alain Lalande Benoit Presles Santiago Romani Domenec Puig |
| author_sort | Vivek Kumar Singh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T08:28:53Z |
| format | Article |
| id | doaj.art-a437eed0236b400f940e8694a32bb5da |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T08:28:53Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a437eed0236b400f940e8694a32bb5da2022-12-21T19:46:46ZengIEEEIEEE Access2169-35362019-01-01713055213056510.1109/ACCESS.2019.29404188832175FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel AttentionVivek Kumar Singh0https://orcid.org/0000-0002-8259-7087Mohamed Abdel-Nasser1Hatem A. Rashwan2https://orcid.org/0000-0001-5421-1637Farhan Akram3Nidhi Pandey4Alain Lalande5Benoit Presles6Santiago Romani7Domenec Puig8Department of Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona, SpainDepartment of Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona, SpainDepartment of Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona, SpainImaging Informatics Division, Bioinformatics Institute, A*STAR, Singapore138671Department of Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona, SpainImViA EA 7535, University of Burgundy, Dijon, FranceImViA EA 7535, University of Burgundy, Dijon, FranceDepartment of Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona, SpainDepartment of Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona, SpainSkin 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.https://ieeexplore.ieee.org/document/8832175/Skin lesionconditional generative adversarial networkchannel attentionfactorized kernelresidual convolution |
| spellingShingle | Vivek Kumar Singh Mohamed Abdel-Nasser Hatem A. Rashwan Farhan Akram Nidhi Pandey Alain Lalande Benoit Presles Santiago Romani Domenec Puig FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention IEEE Access Skin lesion conditional generative adversarial network channel attention factorized kernel residual convolution |
| title | FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention |
| title_full | FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention |
| title_fullStr | FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention |
| title_full_unstemmed | FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention |
| title_short | FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention |
| title_sort | fca net adversarial learning for skin lesion segmentation based on multi scale features and factorized channel attention |
| topic | Skin lesion conditional generative adversarial network channel attention factorized kernel residual convolution |
| url | https://ieeexplore.ieee.org/document/8832175/ |
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