TY  - STD
AU  - Md. Mostafa Kamal Sarker
AU  - Hatem A. Rashwan
AU  - Mohamed Abdel-Nasser
AU  - Vivek Kumar Singh
AU  - Syeda Furruka Banu
AU  - Farhan Akram
AU  - Forhad U. H. Chowdhury
AU  - Kabir Ahmed Choudhury
AU  - Sylvie Chambon
AU  - Petia Radeva
AU  - Domenec Puig
PY  - 2019//
TI  - MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network
N2  - CoRR abs/1907.00856Skin lesion segmentation in dermoscopic images is a challenge due to their blurry and irregular boundaries. Most of the segmentation approaches based on deep learning are time and memory consuming due to the hundreds of millions of parameters. Consequently, it is difficult to apply them to real dermatoscope devices with limited GPU and memory resources. In this paper, we propose a lightweight and efficient Generative Adversarial Networks (GAN) model, called MobileGAN for skin lesion segmentation. More precisely, the MobileGAN combines 1D non-bottleneck factorization networks with position and channel attention modules in a GAN model. The proposed model is evaluated on the test dataset of the ISBI 2017 challenges and the validation dataset of ISIC 2018 challenges. Although the proposed network has only 2.35 millions of parameters, it is still comparable with the state-of-the-art. The experimental results show that our MobileGAN obtains comparable performance with an accuracy of 97.61%.
UR  - https://arxiv.org/abs/1907.00856
N1  - MILAB; no menciona
ID  - Md. Mostafa Kamal Sarker2019
ER  -