Md. Mostafa Kamal Sarker
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks
Sarker, Md. Mostafa Kamal; Rashwan, Hatem A.; Akram, Farhan; Banu, Syeda Furruka; Saleh, Adel; Singh, Vivek Kumar; Chowdhury, Forhad U.H.; Abdulwahab, Saddam; Romani, Santiago; Radeva, Petia; Puig, Domenec
Authors
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
Contributors
Alejandro F. Frangi
Editor
Julia A. Schnabel
Editor
Christos Davatzikos
Editor
Carlos Alberola-L�pez
Editor
Gabor Fictinger
Editor
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 384×384 size per second on a recent GPU.
Citation
SARKER, M.M.K., RASHWAN, H.A., AKRAM, F., BANU, S.F., SALEH, A., SINGH, V.K., CHOWDHURY, F.U.H., ABDULWAHAB, S., ROMANI, S., RADEVA, P. and PUIG, D. 2018. SLSDeep: skin lesion segmentation based on dilated residual and pyramid pooling networks. In Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C. and Fictinger, G. (eds.) Medical image computing and computer assisted intervention (MICCAI 2018): proceedings of 21st international conference on Medical image computing and computer assisted interventions 2018 (MICCAI 2018), 16-20 September 2018, Granada, Spain. Lecture notes in computer science, 11071. Cham: Springer [online], pages 21-29. Available from: https://doi.org/10.1007/978-3-030-00934-2_3
Conference Name | 1st international conference on Medical image computing and computer assisted interventions 2018 (MICCAI 2018) |
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Acceptance Date | Mar 2, 2018 |
Online Publication Date | Sep 26, 2018 |
Publication Date | Dec 31, 2018 |
Deposit Date | Dec 4, 2021 |
Publicly Available Date | Jan 18, 2022 |
Publisher | Springer |
Pages | 21-29 |
Series Title | Lecture notes in computer science (LNCS) |
Series Number | 11071 |
Series ISSN | 0302-9743 |
Book Title | Medical image computing and computer assisted intervention (MICCAI 2018): proceedings of 21st international conference on Medical image computing and computer assisted interventions 2018 (MICCAI 2018), 16-20 September 2018, Granada, Spain |
DOI | https://doi.org/10.1007/978-3-030-00934-2_3 |
Keywords | Skin lesion segmentation melanoma; Deep learning; Dilated residual networks; Pyramid pooling |
Public URL | https://rgu-repository.worktribe.com/output/1542109 |
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Copyright Statement
This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-00934-2_3. Use of this Accepted Version is subject to the publisher's Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms.
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