PT Unknown
AU Md. Mostafa Kamal Sarker
   Hatem A. Rashwan
   Mohamed Abdel-Nasser
   Vivek Kumar Singh
   Syeda Furruka Banu
   Farhan Akram
   Forhad U. H. Chowdhury
   Kabir Ahmed Choudhury
   Sylvie Chambon
   Petia Radeva
   Domenec Puig
TI MobileGAN: Skin Lesion Segmentation Using a Lightweight Generative Adversarial Network
PY 2019
AB 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%.
ER