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

Vivek Kumar Singh (1) , Mohamed Abdel-Nasser (1, 2) , Hatem Rashwan (1) , Farhan Akram (3, 4) , Nidhi Pandey (1) , Alain Lalande (5) , Benoît Presles (5, 6) , Santiago Romani (1) , Domenec Puig (1)

1 CRISES Research Group
2 Department of Electrical Engineering, Faculty of Engineering, Aswan University
3 Department of Electrical and Computer Engineering [Abu Dhabi]
4 Bioinformatics Institute [Singapore]
5 ImViA - Imagerie et Vision Artificielle [Dijon]
6 Bioinformatics Institute [Singapore]

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.

Mots clés: Skin lesion; conditional generative adversarial network; channel attention; factorized kernel; residual convolution; Gradient vector flow; Dermoscopy images

Domaines: Informatique [cs]

Vivek Kumar Singh, Mohamed Abdel-Nasser, Hatem Rashwan, Farhan Akram, Nidhi Pandey, et al.. FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention. IEEE Access, 2019, 7, pp.130552-130565. ⟨10.1109/ACCESS.2019.2940418⟩. ⟨hal-02340216⟩