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Instance Segmentation and Tracking with Cosine Embeddings and Recurrent Hourglass Networks Read chapter Read first chapter

2018 | OriginalPaper | Chapter

SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks

Authors : Md. Mostafa Kamal Sarker, Hatem A. Rashwan, Farhan Akram, Syeda Furruka Banu, Adel Saleh, Vivek Kumar Singh, Forhad U. H. Chowdhury, Saddam Abdulwahab, Santiago Romani, Petia Radeva, Domenec Puig

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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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 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 formulated a new loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the boundaries of melanoma regions. 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 the segmentation accuracy. Moreover, it is capable of segmenting about 100 images of a \(384\times 384\) size per second on a recent GPU.

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previous chapter Skin Lesion Classification in Dermoscopy Images Using Synergic Deep Learning
next chapter -Hemolysis Detection on Cultured Blood Agar Plates by Convolutional Neural Networks
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Metadata
Title
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks
Authors
Md. Mostafa Kamal Sarker
Hatem A. Rashwan
Farhan Akram
Syeda Furruka Banu
Adel Saleh
Vivek Kumar Singh
Forhad U. H. Chowdhury
Saddam Abdulwahab
Santiago Romani
Petia Radeva
Domenec Puig
Copyright Year
2018
Book
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Print ISBN: 978-3-030-00933-5

Electronic ISBN: 978-3-030-00934-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00934-2_3

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