Skin Lesion Segmentation with C-UNet

Junyan Wu; Eric Z. Chen; Ruichen Rong; Xiaoxiao Li; Dong Xu; Hongda Jiang

doi:10.1109/EMBC.2019.8857773

Skin Lesion Segmentation with C-UNet

Junyan Wu, Eric Z. Chen, Ruichen Rong, Xiaoxiao Li, Dong Xu, Hongda Jiang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

27 Scopus citations

Abstract

Malignant melanoma is one of the leading cancers around the world. It is critical to timely diagnose and treat melanoma to improve patient survival. This paper proposes a deep learning model C-UNet for skin lesion segmentation. The C-UNet incorporates the Inception-like convolutional block, the recurrent convolutional block and dilated convolutional layers. We also apply a finetune technique using Dice loss after training the model with commonly used cross-entropy loss. The conditional random field was used to further smooth predicted label maps. Experiment results show that the proposed method achieves better accuracy and more robust segmentation results than UNet.

Original language	English (US)
Title of host publication	2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2785-2788
Number of pages	4
ISBN (Electronic)	9781538613115
DOIs	https://doi.org/10.1109/EMBC.2019.8857773
State	Published - Jul 2019
Event	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany Duration: Jul 23 2019 → Jul 27 2019

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Country/Territory	Germany
City	Berlin
Period	7/23/19 → 7/27/19

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2019.8857773

Cite this

Wu, J., Chen, E. Z., Rong, R., Li, X., Xu, D., & Jiang, H. (2019). Skin Lesion Segmentation with C-UNet. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 2785-2788). Article 8857773 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8857773

Skin Lesion Segmentation with C-UNet. / Wu, Junyan; Chen, Eric Z.; Rong, Ruichen et al.
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2785-2788 8857773 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wu, J, Chen, EZ, Rong, R, Li, X, Xu, D & Jiang, H 2019, Skin Lesion Segmentation with C-UNet. in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019., 8857773, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 2785-2788, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, Berlin, Germany, 7/23/19. https://doi.org/10.1109/EMBC.2019.8857773

Wu J, Chen EZ, Rong R, Li X, Xu D, Jiang H. Skin Lesion Segmentation with C-UNet. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2785-2788. 8857773. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2019.8857773

Wu, Junyan ; Chen, Eric Z. ; Rong, Ruichen et al. / Skin Lesion Segmentation with C-UNet. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2785-2788 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Skin Lesion Segmentation with C-UNet",

abstract = "Malignant melanoma is one of the leading cancers around the world. It is critical to timely diagnose and treat melanoma to improve patient survival. This paper proposes a deep learning model C-UNet for skin lesion segmentation. The C-UNet incorporates the Inception-like convolutional block, the recurrent convolutional block and dilated convolutional layers. We also apply a finetune technique using Dice loss after training the model with commonly used cross-entropy loss. The conditional random field was used to further smooth predicted label maps. Experiment results show that the proposed method achieves better accuracy and more robust segmentation results than UNet.",

author = "Junyan Wu and Chen, {Eric Z.} and Ruichen Rong and Xiaoxiao Li and Dong Xu and Hongda Jiang",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 ; Conference date: 23-07-2019 Through 27-07-2019",

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N2 - Malignant melanoma is one of the leading cancers around the world. It is critical to timely diagnose and treat melanoma to improve patient survival. This paper proposes a deep learning model C-UNet for skin lesion segmentation. The C-UNet incorporates the Inception-like convolutional block, the recurrent convolutional block and dilated convolutional layers. We also apply a finetune technique using Dice loss after training the model with commonly used cross-entropy loss. The conditional random field was used to further smooth predicted label maps. Experiment results show that the proposed method achieves better accuracy and more robust segmentation results than UNet.

AB - Malignant melanoma is one of the leading cancers around the world. It is critical to timely diagnose and treat melanoma to improve patient survival. This paper proposes a deep learning model C-UNet for skin lesion segmentation. The C-UNet incorporates the Inception-like convolutional block, the recurrent convolutional block and dilated convolutional layers. We also apply a finetune technique using Dice loss after training the model with commonly used cross-entropy loss. The conditional random field was used to further smooth predicted label maps. Experiment results show that the proposed method achieves better accuracy and more robust segmentation results than UNet.

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Skin Lesion Segmentation with C-UNet

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