@InProceedings{Md.MostafaKamalSarker2018,
author="Md.Mostafa Kamal Sarker
and Hatem, A. Rashwan
and Farhan Akram
and Syeda Furruka Banu
and Adel Saleh
and Vivek Kumar Singh
and Forhad U. H. Chowdhury
and Saddam Abdulwahab
and Santiago Romani
and Petia Radeva
and Domenec Puig",
title="SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks.",
booktitle="21st International Conference on Medical Image Computing \& Computer Assisted Intervention",
year="2018",
volume="2",
pages="21--29",
abstract="Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than 100 images of size 384x384 per second on a recent GPU.",
optnote="MILAB; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3112), last updated on Mon, 24 Jan 2022 13:27:20 +0100",
opturl="https://link.springer.com/chapter/10.1007\%2F978-3-030-00934-2_3",
file=":http://refbase.cvc.uab.es/files/SRA2018.pdf:PDF"
}