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

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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Bibliographic Details
Main Authors: Vivek Kumar Singh, Mohamed Abdel-Nasser, Hatem A. Rashwan, Farhan Akram, Nidhi Pandey, Alain Lalande, Benoit Presles, Santiago Romani, Domenec Puig
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Skin lesion
conditional generative adversarial network
channel attention
factorized kernel
residual convolution
Online Access:https://ieeexplore.ieee.org/document/8832175/

Internet

https://ieeexplore.ieee.org/document/8832175/

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